APP for Monitoring Skin Spots,
2019.
WWW Link.
Code, Skin Spots.
1906
he app allows you to:
take and organise photos of spots;
compare two images of a spot side by side;
email those images to someone (eg. doctor).
That's all!
Xu, L.,
Jackowski, M.,
Goshtasby, A.,
Roseman, D.,
Bines, S.,
Yu, C.,
Dhawan, A.,
Huntley, A.,
Segmentation skin cancer images,
IVC(17), No. 1, January 1999, pp. 65-74.
Elsevier DOI
Code, Segmentation. Software describe here is available from:
HTML Version.
BibRef
9901
Golston, J.E.,
Moss, R.H.,
Stoecker, W.V.,
Boundary detection in skin tumor images:
An overall approach and a radial search algorithm,
PR(23), No. 11, 1990, pp. 1235-1247.
Elsevier DOI
0401
BibRef
Zhang, Z.,
Stoecker, W.V.,
Moss, R.H.,
Border detection on digitized skin tumor images,
MedImg(19), No. 11, November 2000, pp. 1128-1143.
IEEE Top Reference.
0110
BibRef
Ganster, H.,
Pinz, P.,
Rohrer, R.,
Wildling, E.,
Binder, M.,
Kittler, H.,
Automated melanoma recognition,
MedImg(20), No. 3, March 2001, pp. 233-239.
IEEE Top Reference.
0110
BibRef
Xu, S.C.[Shu-Chang],
Ye, X.Z.[Xiu-Zi],
Wu, Y.[Yin],
Giron, F.[Franck],
Leveque, J.L.[Jean-Luc],
Querleux, B.[Bernard],
Automatic skin decomposition based on single image,
CVIU(110), No. 1, April 2008, pp. 1-6.
Elsevier DOI
0804
Skin decomposition; Melanin; Hemoglobin; Image process
BibRef
Gomez, D.D.[David Delgado],
Carstensen, J.M.[Jens Michael],
Ersbøll, B.K.[Bjarne K.],
Collecting highly reproducible images to support dermatological medical
diagnosis,
IVC(24), No. 2, 1 February 2006, pp. 186-191.
Elsevier DOI
0604
Image acquisition; Camera calibration; Diffuse illumination;
Principal component analysis; Independent component analysis; Psoriasis
BibRef
Taur, J.S.[Jinshiuh S.],
Lee, G.H.[Gwo-Her],
Tao, C.W.,
Chen, C.C.[Chien-Chou],
Yang, C.W.[Ching-Wen],
Segmentation of Psoriasis Vulgaris Images Using Multiresolution-Based
Orthogonal Subspace Techniques,
SMC-B(36), No. 2, April 2006, pp. 390-402.
IEEE DOI
0604
BibRef
Tang, J.S.[Jin-Shan],
A multi-direction GVF snake for the segmentation of skin cancer images,
PR(42), No. 6, June 2009, pp. 1172-1179.
Elsevier DOI
0902
Skin tumor; Boundary extraction; Gradient vector flow; Active contour;
Multidirection
BibRef
Serrano, C.[Carmen],
Acha, B.[Begona],
Pattern analysis of dermoscopic images based on Markov random fields,
PR(42), No. 6, June 2009, pp. 1052-1057.
Elsevier DOI
0902
Dermoscopic images; Pattern classification; Markov random field
BibRef
Jiménez, A.[Amaya],
Serrano, C.[Carmen],
Acha, B.[Begoña],
Automatic Detection of Globules, Streaks and Pigment Network Based on
Texture and Color Analysis in Dermoscopic Images,
ICIAR17(486-493).
Springer DOI
1706
BibRef
Mendoza, C.S.[Carlos S.],
Serrano, C.[Carmen],
Acha, B.[Begoña],
Pattern Analysis of Dermoscopic Images Based on FSCM Color Markov
Random Fields,
ACIVS09(676-685).
Springer DOI
0909
BibRef
Kallel, I.F.[Imen Fourati],
Bouhlel, M.S.[Mohamed Salim],
Lapayre, J.C.[Jean-Christophe],
Garcia, E.[Eric],
Control of dermatology image integrity using reversible watermarking,
IJIST(19), No. 1, March 2009, pp. 5-9.
DOI Link
0902
BibRef
Peserico, E.[Enoch],
Silletti, A.[Alberto],
Is (N)PRI suitable for evaluating automated segmentation of cutaneous
lesions?,
PRL(31), No. 16, December 2010, pp. 2464-2467.
Elsevier DOI
1011
Segmentation; Evaluation; NPRI; Melanoma
BibRef
La Torre, E.[Elisabetta],
Caputo, B.[Barbara],
Tommasi, T.[Tatiana],
Learning methods for melanoma recognition,
IJIST(20), No. 4, December 2010, pp. 316-322.
DOI Link
1011
BibRef
Earlier: A3, A1, A2:
Melanoma Recognition Using Representative and Discriminative Kernel
Classifiers,
CVAMIA06(1-12).
Springer DOI
0605
See also Discriminative cue integration for medical image annotation.
BibRef
Capdehourat, G.[Germán],
Corez, A.[Andrés],
Bazzano, A.[Anabella],
Alonso, R.[Rodrigo],
Musé, P.[Pablo],
Toward a combined tool to assist dermatologists in melanoma detection
from dermoscopic images of pigmented skin lesions,
PRL(32), No. 16, 1 December 2011, pp. 2187-2196.
Elsevier DOI
1112
Melanoma; Dermoscopy; Pigmented skin lesion classification; Adaptive
boosting; Support vector machines; Decision trees
BibRef
Khan, R.[Rehanullah],
Hanbury, A.[Allan],
Stöttinger, J.[Julian],
Bais, A.[Abdul],
Color based skin classification,
PRL(33), No. 2, 15 January 2012, pp. 157-163.
Elsevier DOI
1112
Skin detection; Skin classification; Color spaces and skin detection;
Color constancy
BibRef
Silva, C.S.P.[Cátia S. P.],
Marcal, A.R.S.[André R. S.],
Pereira, M.A.[Marta A.],
Mendonça, T.[Teresa],
Rozeira, J.[Jorge],
Separability Analysis of Color Classes on Dermoscopic Images,
ICIAR12(II: 268-277).
Springer DOI
1206
BibRef
Zhou, Y.[Yu],
Smith, M.L.[Melvyn L.],
Smith, L.N.[Lyndon N.],
Warr, R.[Robert],
Combinatorial photometric stereo and its application in 3D modeling of
melanoma,
MVA(23), No. 5, September 2012, pp. 1029-1045.
WWW Link.
1208
BibRef
Ma, L.[Li],
Staunton, R.C.[Richard C.],
Analysis of the contour structural irregularity of skin lesions using
wavelet decomposition,
PR(46), No. 1, January 2013, pp. 98-106.
Elsevier DOI
1209
Melanoma detection; Structural irregularity of contours; Wavelet
decomposition; Multi-scale descriptors; Significant wavelet sub-bands
BibRef
Abbas, Q.[Qaisar],
Celebi, M.E.,
Serrano, C.[Carmen],
García, I.F.[Irene Fondón],
Ma, G.[Guangzhi],
Pattern classification of dermoscopy images:
A perceptually uniform model,
PR(46), No. 1, January 2013, pp. 86-97.
Elsevier DOI
1209
Dermoscopy; Pattern classification; Steerable pyramid transform; Human
visual system; AdaBoost; Multi-label learning
BibRef
Lu, C.[Cheng],
Mahmood, M.[Muhammad],
Jha, N.[Naresh],
Mandal, M.[Mrinal],
Detection of melanocytes in skin histopathological images using radial
line scanning,
PR(46), No. 2, February 2013, pp. 509-518.
Elsevier DOI
1210
Histopathological image analysis; Object detection; Image analysis;
Melanocytes
BibRef
Lu, C.[Cheng],
Mandal, M.[Mrinal],
Automated analysis and diagnosis of skin melanoma on whole slide
histopathological images,
PR(48), No. 8, 2015, pp. 2738-2750.
Elsevier DOI
1505
Histopathological image analysis
BibRef
d'Alessandro, B.,
Dhawan, A.P.,
3-D Volume Reconstruction of Skin Lesions for Melanin and Blood Volume
Estimation and Lesion Severity Analysis,
MedImg(31), No. 11, November 2012, pp. 2083-2092.
IEEE DOI
1211
BibRef
Smaoui, N.[Nadia],
Bessassi, S.[Souhir],
A Developed System for Melanoma Diagnosis,
IJCVSP(3), No. 1, 2013, pp. xx-yy.
WWW Link.
1309
BibRef
Scharcanski, J.[Jacob],
Celebi, M.E.[M. Emre], (Eds.),
Computer Vision Techniques for the Diagnosis of Skin Cancer,
Saez, A.,
Serrano, C.,
Acha, B.,
Model-Based Classification Methods of Global Patterns in Dermoscopic
Images,
MedImg(33), No. 5, May 2014, pp. 1137-1147.
IEEE DOI
1405
Classification
BibRef
Serna, A.[Andrés],
Marcotegui, B.[Beatriz],
Decencière, E.[Etienne],
Baldeweck, T.[Thérèse],
Pena, A.M.[Ana-Maria],
Brizion, S.[Sébastien],
Segmentation of elongated objects using attribute profiles and area
stability: Application to melanocyte segmentation in engineered skin,
PRL(47), No. 1, 2014, pp. 172-182.
Elsevier DOI
1408
Mathematical morphology
BibRef
Lu, Y.,
Xie, F.,
Wu, Y.,
Jiang, Z.,
Meng, R.,
No Reference Uneven Illumination Assessment for Dermoscopy Images,
SPLetters(22), No. 5, May 2015, pp. 534-538.
IEEE DOI
1411
Algorithm design and analysis
BibRef
Schwarz, M.,
Omar, M.,
Buehler, A.,
Aguirre, J.,
Ntziachristos, V.,
Implications of Ultrasound Frequency in Optoacoustic Mesoscopy of the
Skin,
MedImg(34), No. 2, February 2015, pp. 672-677.
IEEE DOI
1502
Dermis
BibRef
Jiji, G.W.[Gnanasigamony Wiselin],
Raj, P.S.J.D.[Peter Savariraj Johnson Durai],
Content-based image retrieval in dermatology using intelligent
technique,
IET-IPR(9), No. 4, 2015, pp. 306-317.
DOI Link
1505
content-based retrieval
BibRef
Ledoux, A.[Audrey],
Richard, N.[Noël],
Capelle-Laizé, A.S.[Anne-Sophie],
Fernandez-Maloigne, C.[Christine],
Perceptual color hit-or-miss transform:
Application to dermatological image processing,
SIViP(9), No. 5, July 2015, pp. 1081-1091.
Springer DOI
1506
BibRef
Xu, H.M.[Hong-Ming],
Mandal, M.[Mrinal],
Epidermis segmentation in skin histopathological images based on
thickness measurement and k-means algorithm,
JIVP(2015), No. 1, 2015, pp. 18.
DOI Link
1507
BibRef
Lu, C.[Cheng],
Ma, Z.[Zhen],
Mandal, M.,
Automated segmentation of the epidermis area in skin whole slide
histopathological images,
IET-IPR(9), No. 9, 2015, pp. 735-742.
DOI Link
1509
biomedical optical imaging
BibRef
Kruk, M.[Michal],
Swiderski, B.[Bartosz],
Osowski, S.[Stanislaw],
Kurek, J.[Jaroslaw],
Slowinska, M.[Monika],
Walecka, I.[Irena],
Melanoma recognition using extended set of descriptors and
classifiers,
JIVP(2015), No. 1, 2015, pp. 43.
DOI Link
1601
BibRef
Sáez, A.,
Sánchez-Monedero, J.,
Gutiérrez, P.A.,
Hervás-Martínez, C.,
Machine Learning Methods for Binary and Multiclass Classification of
Melanoma Thickness From Dermoscopic Images,
MedImg(35), No. 4, April 2016, pp. 1036-1045.
IEEE DOI
1604
Color
BibRef
Bae, J.S.[Ji-Sang],
Jeon, J.H.[Jae-Ho],
Lee, J.Y.[Jae-Young],
Kim, J.O.[Jong-Ok],
Skin Condition Estimation Using Mobile Handheld Camera,
ETRI(38), No. 4, August 2016, pp. 776-786.
DOI Link
1608
BibRef
Noroozi, N.[Navid],
Zakerolhosseini, A.[Ali],
Computer assisted diagnosis of basal cell carcinoma using Z-transform
features,
JVCIR(40, Part A), No. 1, 2016, pp. 128-148.
Elsevier DOI
1609
Skin cancer
BibRef
Barata, C.[Catarina],
Celebi, M.E.[M. Emre],
Marques, J.S.[Jorge S.],
Rozeira, J.[Jorge],
Clinically inspired analysis of dermoscopy images using a generative
model,
CVIU(151), No. 1, 2016, pp. 124-137.
Elsevier DOI
1610
BibRef
Earlier: A1, A3, A2, Only:
Improving dermoscopy image analysis using color constancy,
ICIP14(3527-3531)
IEEE DOI
1502
Melanoma.
Design automation
BibRef
Ghanta, S.,
Jordan, M.I.,
Kose, K.,
Brooks, D.H.,
Rajadhyaksha, M.,
Dy, J.G.,
A Marked Poisson Process Driven Latent Shape Model for 3D
Segmentation of Reflectance Confocal Microscopy Image Stacks of Human
Skin,
IP(26), No. 1, January 2017, pp. 172-184.
IEEE DOI
1612
Markov processes
BibRef
Ferri, M.[Massimo],
Tomba, I.[Ivan],
Visotti, A.[Andrea],
Stanganelli, I.[Ignazio],
A Feasibility Study for a Persistent Homology-Based k-Nearest Neighbor
Search Algorithm in Melanoma Detection,
JMIV(57), No. 3, March 2017, pp. 324-339.
Springer DOI
1702
BibRef
Xie, F.,
Fan, H.,
Li, Y.,
Jiang, Z.,
Meng, R.,
Bovik, A.,
Melanoma Classification on Dermoscopy Images Using a Neural Network
Ensemble Model,
MedImg(36), No. 3, March 2017, pp. 849-858.
IEEE DOI
1703
Cancer
BibRef
Yu, L.,
Chen, H.,
Dou, Q.,
Qin, J.,
Heng, P.A.,
Automated Melanoma Recognition in Dermoscopy Images via Very Deep
Residual Networks,
MedImg(36), No. 4, April 2017, pp. 994-1004.
IEEE DOI
1704
Biomedical imaging
BibRef
Roy, A.[Anandarup],
Pal, A.[Anabik],
Garain, U.[Utpal],
JCLMM: A finite mixture model for clustering of circular-linear data
and its application to psoriatic plaque segmentation,
PR(66), No. 1, 2017, pp. 160-173.
Elsevier DOI
1704
BibRef
And:
Erratum:
PR(77), 2018, pp. 464.
Elsevier DOI
1802
Mixture model
BibRef
Barata, C.[Catarina],
Celebi, M.E.[M. Emre],
Marques, J.S.[Jorge S.],
Development of a clinically oriented system for melanoma diagnosis,
PR(69), No. 1, 2017, pp. 270-285.
Elsevier DOI
1706
Melanoma, diagnosis
BibRef
Ohki, K.[Keiichi],
Celebi, M.E.[M. Emre],
Schaefer, G.[Gerald],
Iyatomi, H.[Hitoshi],
Building of Readable Decision Trees for Automated Melanoma
Discrimination,
ISVC15(II: 712-721).
Springer DOI
1601
BibRef
Schwarz, M.,
Soliman, D.,
Omar, M.,
Buehler, A.,
Ovsepian, S.V.,
Aguirre, J.,
Ntziachristos, V.,
Optoacoustic Dermoscopy of the Human Skin: Tuning Excitation Energy
for Optimal Detection Bandwidth With Fast and Deep Imaging in vivo,
MedImg(36), No. 6, June 2017, pp. 1287-1296.
IEEE DOI
1706
Attenuation, Bandwidth, Detectors, Frequency response, Phantoms, Skin,
Angiographic imaging, evaluation and performance,
image quality assessment, optimization,
optoacoustic/photo-acoustic imaging, skin, tissue modelling,
vessels, visualization
BibRef
Bae, J.S.[Ji-Sang],
Lee, S.H.[Sang-Ho],
Choi, K.S.[Kang-Sun],
Kim, J.O.[Jong-Ok],
Robust skin-roughness estimation based on co-occurrence matrix,
JVCIR(46), No. 1, 2017, pp. 13-22.
Elsevier DOI
1706
Skin, roughness
BibRef
Sadri, A.R.[Amir Reza],
Azarianpour, S.[Sepideh],
Zekri, M.[Maryam],
Celebi, M.E.[Mehmet Emre],
Sadri, S.[Saeid],
WN-based approach to melanoma diagnosis from dermoscopy images,
IET-IPR(11), No. 7, July 2017, pp. 475-482.
DOI Link
1707
BibRef
Yuan, Y.,
Chao, M.,
Lo, Y.C.,
Automatic Skin Lesion Segmentation Using Deep Fully Convolutional
Networks With Jaccard Distance,
MedImg(36), No. 9, September 2017, pp. 1876-1886.
IEEE DOI
1709
cancer, endoscopes, neural nets, skin, tumours, Jaccard distance,
automatic skin lesion segmentation, melanoma detection challenge,
Malignant tumors,
BibRef
Adjed, F.[Faouzi],
Gardezi, S.J.S.[Syed Jamal Safdar],
Ababsa, F.[Fakhreddine],
Faye, I.[Ibrahima],
Dass, S.C.[Sarat Chandra],
Fusion of structural and textural features for melanoma recognition,
IET-CV(12), No. 2, March 2018, pp. 185-195.
DOI Link
1804
BibRef
Bi, L.[Lei],
Kim, J.M.[Jin-Man],
Ahn, E.[Euijoon],
Kumar, A.[Ashnil],
Feng, D.D.[David Dagan],
Fulham, M.[Michael],
Step-wise integration of deep class-specific learning for dermoscopic
image segmentation,
PR(85), 2019, pp. 78-89.
Elsevier DOI
1810
Dermoscopic, Melanoma, Segmentation, Fully convolutional networks (FCN)
BibRef
Ahn, E.[Euijoon],
Kumar, A.[Ashnil],
Fulham, M.[Michael],
Feng, D.D.[David Dagan],
Kim, J.M.[Jin-Man],
Unsupervised Domain Adaptation to Classify Medical Images Using
Zero-Bias Convolutional Auto-Encoders and Context-Based Feature
Augmentation,
MedImg(39), No. 7, July 2020, pp. 2385-2394.
IEEE DOI
2007
Biomedical imaging, Feature extraction, Training, Skin, Diseases,
Training data, Convolutional auto-encoders,
unsupervised feature learning
BibRef
Do, T.,
Hoang, T.,
Pomponiu, V.,
Zhou, Y.,
Chen, Z.,
Cheung, N.,
Koh, D.,
Tan, A.,
Tan, S.,
Accessible Melanoma Detection Using Smartphones and Mobile Image
Analysis,
MultMed(20), No. 10, October 2018, pp. 2849-2864.
IEEE DOI
1810
Lesions, Skin, Malignant tumors, Feature extraction, Smart phones,
Image color analysis, Image analysis,
human-computer interface
BibRef
Xia, Y.,
Zhang, L.,
Meng, L.,
Yan, Y.,
Nie, L.,
Li, X.,
Exploring Web Images to Enhance Skin Disease Analysis Under A
Computer Vision Framework,
Cyber(48), No. 11, November 2018, pp. 3080-3091.
IEEE DOI
1810
Skin, Diseases, Visualization, Training, Hospitals, Feature extraction,
Skin disease inference, transfer learning
BibRef
Sultana, N.N.[Nazneen N.],
Mandal, B.[Bappaditya],
Puhan, N.B.,
Deep residual network with regularised fisher framework for detection
of melanoma,
IET-CV(12), No. 8, December 2018, pp. 1096-1104.
DOI Link
1812
BibRef
Nguyen, K.L.,
Delachartre, P.,
Berthier, M.,
Multi-Grid Phase Field Skin Tumor Segmentation in 3D Ultrasound
Images,
IP(28), No. 8, August 2019, pp. 3678-3687.
IEEE DOI
1907
biomedical ultrasonics, image segmentation,
medical image processing, nonparametric statistics, skin, tumours,
exact solutions
BibRef
Zhang, J.,
Xie, Y.,
Xia, Y.,
Shen, C.,
Attention Residual Learning for Skin Lesion Classification,
MedImg(38), No. 9, September 2019, pp. 2092-2103.
IEEE DOI
1909
Lesions, Skin, Melanoma, Learning systems, Task analysis,
Visualization, Computational modeling, Attention learning,
dermoscopy images
BibRef
Mirbeik-Sabzevari, A.,
Oppelaar, E.,
Ashinoff, R.,
Tavassolian, N.,
High-Contrast, Low-Cost, 3-D Visualization of Skin Cancer Using
Ultra-High-Resolution Millimeter-Wave Imaging,
MedImg(38), No. 9, September 2019, pp. 2188-2197.
IEEE DOI
1909
Imaging, Skin cancer, Surgery, Tumors, Millimeter wave technology,
Skin, Millimeter-wave imaging, skin cancer imaging, malignant skin tissue
BibRef
Sarkar, R.[Rahul],
Chatterjee, C.C.[Chandra Churh],
Hazra, A.[Animesh],
Diagnosis of melanoma from dermoscopic images using a deep depthwise
separable residual convolutional network,
IET-IPR(13), No. 12, October 2019, pp. 2130-2142.
DOI Link
1911
BibRef
Khan, M.A.[Muhammad Attique],
Sharif, M.[Muhammad],
Akram, T.[Tallha],
Bukhari, S.A.C.[Syed Ahmad Chan],
Nayak, R.S.[Ramesh Sunder],
Developed Newton-Raphson based deep features selection framework for
skin lesion recognition,
PRL(129), 2020, pp. 293-303.
Elsevier DOI
2001
Skin cancer, Contrast stretching, Lesion localization,
Deep features, Best features
BibRef
Wang, X.,
Jiang, X.,
Ding, H.,
Liu, J.,
Bi-Directional Dermoscopic Feature Learning and Multi-Scale
Consistent Decision Fusion for Skin Lesion Segmentation,
IP(29), 2020, pp. 3039-3051.
IEEE DOI
2002
Lesions, Skin, Image segmentation, Melanoma, Bidirectional control,
Reliability, Feature extraction, Skin lesion segmentation,
multi-scale consistent decision fusion
BibRef
Amin, J.[Javeria],
Sharif, A.[Abida],
Gul, N.[Nadia],
Anjum, M.A.[Muhammad Almas],
Nisar, M.W.[Muhammad Wasif],
Azam, F.[Faisal],
Bukhari, S.A.C.[Syed Ahmad Chan],
Integrated design of deep features fusion for localization and
classification of skin cancer,
PRL(131), 2020, pp. 63-70.
Elsevier DOI
2004
Cells, Principle component analysis (PCA), Melanoma, Alexnet, VGG-16
BibRef
Jayapriya, K.[Kalyanakumar],
Jacob, I.J.[Israel Jeena],
Hybrid fully convolutional networks-based skin lesion segmentation
and melanoma detection using deep feature,
IJIST(30), No. 2, 2020, pp. 348-357.
DOI Link
2005
deep residual network (DRN),
hybrid fully convolutional networks, melanoma detection
BibRef
Zhou, W.,
Chen, Z.,
Zhou, Q.,
Xing, D.,
Optical Biopsy of Melanoma and Basal Cell Carcinoma Progression by
Noncontact Photoacoustic and Optical Coherence Tomography: In Vivo
Multi-Parametric Characterizing Tumor Microenvironment,
MedImg(39), No. 6, June 2020, pp. 1967-1974.
IEEE DOI
2006
Photoacoustic imaging, OCT, skin, multi-modality fusion, integration of multiscale information
BibRef
Phan, T.B.[Tan-Binh],
Trinh, D.H.[Dinh-Hoan],
Wolf, D.[Didier],
Daul, C.[Christian],
Optical flow-based structure-from-motion for the reconstruction of
epithelial surfaces,
PR(105), 2020, pp. 107391.
Elsevier DOI
2006
3D Image mosaicing, Structure-from-Motion (SfM),
Dense optical flow, Endoscopy, Dermatology
BibRef
Xiao, F.[Feng],
Wu, Q.[Qiuxia],
Visual saliency based global-local feature representation for skin
cancer classification,
IET-IPR(14), No. 10, August 2020, pp. 2140-2148.
DOI Link
2008
BibRef
Bi, L.[Lei],
Feng, D.D.[David Dagan],
Fulham, M.[Michael],
Kim, J.M.[Jin-Man],
Multi-Label classification of multi-modality skin lesion via
hyper-connected convolutional neural network,
PR(107), 2020, pp. 107502.
Elsevier DOI
2008
Classification, Melanoma, Convolutional neural networks (cnns)
BibRef
Fu, X.H.[Xiao-Hang],
Bi, L.[Lei],
Kumar, A.[Ashnil],
Fulham, M.[Michael],
Kim, J.M.[Jin-Man],
Graph-Based Intercategory and Intermodality Network for Multilabel
Classification and Melanoma Diagnosis of Skin Lesions in Dermoscopy
and Clinical Images,
MedImg(41), No. 11, November 2022, pp. 3266-3277.
IEEE DOI
2211
Lesions, Skin, Feature extraction, Visualization, Melanoma, Topology,
Correlation, Classification, dermoscopy, melanoma, multimodal image
BibRef
Divya, D.,
Ganeshbabu, T.R.,
Fitness adaptive deer hunting-based region growing and recurrent
neural network for melanoma skin cancer detection,
IJIST(30), No. 3, 2020, pp. 731-752.
DOI Link
2008
dermoscopic image, melanoma skin cancer,
modified deer hunting algorithm, optimized region growing,
recurrent neural network
BibRef
Kalra, M.[Meeta],
Bouguila, N.[Nizar],
Fan, W.T.[Wen-Tao],
Online variational learning of finite inverted Beta-Liouville mixture
model for biomedical analysis,
IJIST(30), No. 3, 2020, pp. 794-814.
DOI Link
2008
biomedical images, brain tumor detection, CAD of malaria,
colon cancer, diabetic retinopathy, feature extraction,
skin melanoma
BibRef
Genina, E.A.,
Surkov, Y.I.,
Serebryakova, I.A.,
Bashkatov, A.N.,
Tuchin, V.V.,
Zharov, V.P.,
Rapid Ultrasound Optical Clearing of Human Light and Dark Skin,
MedImg(39), No. 10, October 2020, pp. 3198-3206.
IEEE DOI
2010
Skin, Optical imaging, Biomedical optical imaging,
Optical refraction, Optical variables control, sonophoresis
BibRef
Wang, Q.,
Sun, L.,
Wang, Y.,
Zhou, M.,
Hu, M.,
Chen, J.,
Wen, Y.,
Li, Q.,
Identification of Melanoma From Hyperspectral Pathology Image Using
3D Convolutional Networks,
MedImg(40), No. 1, January 2021, pp. 218-227.
IEEE DOI
2012
Microscopy, segmentation, skin, quantification and estimation, optical imaging
BibRef
Khan, M.A.[Muhammad Attique],
Akram, T.[Tallha],
Zhang, Y.D.[Yu-Dong],
Sharif, M.[Muhammad],
Attributes based skin lesion detection and recognition: A mask RCNN
and transfer learning-based deep learning framework,
PRL(143), 2021, pp. 58-66.
Elsevier DOI
2102
Skin cancer, Mask RCNN, Transfer learning, Optimal features, ELM
BibRef
Jiji, G.W.[G. Wiselin],
Rajesh, A.,
Raj, P.J.D.[P. Johnson Durai],
CBI + R: A Fusion Approach to Assist Dermatological Diagnoses,
IJIG(21), No. 1 2021, pp. 2150005.
DOI Link
2102
BibRef
Zhang, B.,
Wang, Z.,
Gao, J.,
Rutjes, C.,
Nufer, K.,
Tao, D.,
Feng, D.D.,
Menzies, S.W.,
Short-Term Lesion Change Detection for Melanoma Screening With Novel
Siamese Neural Network,
MedImg(40), No. 3, March 2021, pp. 840-851.
IEEE DOI
2103
Deep learning, Image segmentation, Neural networks, Melanoma,
Feature extraction, Lesions, Task analysis, Melanoma screening,
deep learning
BibRef
Mishra, S.[Sourav],
Chaudhury, S.[Subhajit],
Imaizumi, H.[Hideaki],
Yamasaki, T.[Toshihiko],
Robustness of Deep Learning Models in Dermatological Evaluation:
A Critical Assessment,
IEICE(E104-D), No. 3, March 2021, pp. 419-429.
WWW Link.
2103
BibRef
Majji, R.[Ramachandro],
Prakash, P.G.O.[Ponnusamy Gnanaprakasam Om],
Cristin, R.[Rajan],
Parthasarathy, G.[Govindaswamy],
Social bat optimisation dependent deep stacked auto-encoder for skin
cancer detection,
IET-IPR(14), No. 16, 19 December 2020, pp. 4122-4131.
DOI Link
2103
BibRef
Dremin, V.,
Marcinkevics, Z.,
Zherebtsov, E.,
Popov, A.,
Grabovskis, A.,
Kronberga, H.,
Geldnere, K.,
Doronin, A.,
Meglinski, I.,
Bykov, A.,
Skin Complications of Diabetes Mellitus Revealed by Polarized
Hyperspectral Imaging and Machine Learning,
MedImg(40), No. 4, April 2021, pp. 1207-1216.
IEEE DOI
2104
Skin, Diabetes, Hyperspectral imaging, Blood, Aging,
Biological tissues, Medical diagnostic imaging,
skin complications
BibRef
Thiyaneswaran, B.,
Anguraj, K.,
Kumarganesh, S.,
Thangaraj, K.,
Early detection of melanoma images using gray level co-occurrence
matrix features and machine learning techniques for effective
clinical diagnosis,
IJIST(31), No. 2, 2021, pp. 682-694.
DOI Link
2105
COVID, FFBPNN, gamma correction, GLCM, HSI, K-means, SVM
BibRef
Alizadeh, S.M.[Seyed Mohammad],
Mahloojifar, A.[Ali],
Automatic skin cancer detection in dermoscopy images by combining
convolutional neural networks and texture features,
IJIST(31), No. 2, 2021, pp. 695-707.
DOI Link
2105
computer-aided diagnosis, convolutional neural networks,
kernel principal component analysis, melanoma, skin cancer, texture feature
BibRef
Gautam, A.[Anjali],
Raman, B.[Balasubramanian],
Towards accurate classification of skin cancer from dermatology
images,
IET-IPR(15), No. 9, 2021, pp. 1971-1986.
DOI Link
2106
BibRef
Ashour, A.S.[Amira S.],
Wahba, M.A.[Maram A.],
Alaa, E.E.[Eman Elsaid],
Guo, Y.H.[Yan-Hui],
Hawas, A.R.[Ahmed Refaat],
A novel diagnostic map for computer-aided diagnosis of skin cancer,
IET-IPR(15), No. 4, 2021, pp. 897-907.
DOI Link
2106
BibRef
Durgarao, N.[Nagayalanka],
Sudhavani, G.[Ghanta],
Diagnosing skin cancer via C-means segmentation with enhanced fuzzy
optimization,
IET-IPR(15), No. 10, 2021, pp. 2266-2280.
DOI Link
2108
BibRef
Pollastri, F.[Federico],
Parreño, M.[Mario],
Maroñas, J.[Juan],
Bolelli, F.[Federico],
Paredes, R.[Roberto],
Ramos, D.[Daniel],
Grana, C.[Costantino],
A deep analysis on high-resolution dermoscopic image classification,
IET-CV(15), No. 7, 2021, pp. 514-526.
DOI Link
2109
BibRef
Ding, B.J.[Bao-Jin],
Wang, H.X.[Hai-Xia],
Chen, P.[Peng],
Zhang, Y.L.[Yi-Long],
Liang, R.H.[Rong-Hua],
Liu, Y.P.[Yi-Peng],
Subcutaneous sweat pore estimation from optical coherence tomography,
IET-IPR(15), No. 13, 2021, pp. 3267-3280.
DOI Link
2110
BibRef
Zhu, W.[Wei],
Li, W.B.[Wen-Bin],
Liao, H.F.[Hao-Fu],
Luo, J.B.[Jie-Bo],
Temperature network for few-shot learning with distribution-aware
large-margin metric,
PR(112), 2021, pp. 107797.
Elsevier DOI
2102
Few-shot learning, Metric learning, Skin lesion classification,
Temperature function
BibRef
Thepade, S.D.[Sudeep D.],
Ramnani, G.[Gaurav],
Mandhare, S.[Shubham],
Melanoma skin cancer identification with amalgamated TSBTC and BTC
colour features using ensemble of machine learning algorithms,
IJCVR(11), No. 6, 2021, pp. 616-639.
DOI Link
2111
BibRef
Choudhary, P.[Priya],
Singhai, J.[Jyoti],
Yadav, J.S.,
Curvelet and fast marching method-based technique for efficient
artifact detection and removal in dermoscopic images,
IJIST(31), No. 4, 2021, pp. 2334-2345.
DOI Link
2112
curvelet, fast marching method, Frangi vesselness, melanoma
BibRef
Sharma, M.[Moolchand],
Jain, B.[Bhanu],
Kargeti, C.[Chetan],
Gupta, V.[Vinayak],
Gupta, D.[Deepak],
Detection and Diagnosis of Skin Diseases Using Residual Neural Networks
(RESNET),
IJIG(21), No. 5 2021, pp. 2140002.
DOI Link
2201
BibRef
Rajput, G.[Gunjan],
Agrawal, S.[Shashank],
Raut, G.[Gopal],
Vishvakarma, S.K.[Santosh Kumar],
An accurate and noninvasive skin cancer screening based on imaging
technique,
IJIST(32), No. 1, 2022, pp. 354-368.
DOI Link
2201
convolutional neural network, deep learning, detection,
HAM10000 dataset, skin cancer classification
BibRef
Yu, Z.[Zhen],
Nguyen, J.[Jennifer],
Nguyen, T.D.[Toan D.],
Kelly, J.[John],
Mclean, C.[Catriona],
Bonnington, P.[Paul],
Zhang, L.[Lei],
Mar, V.[Victoria],
Ge, Z.Y.[Zong-Yuan],
Early Melanoma Diagnosis With Sequential Dermoscopic Images,
MedImg(41), No. 3, March 2022, pp. 633-646.
IEEE DOI
2203
Lesions, Melanoma, Skin, Convolutional neural networks,
Feature extraction, Cancer, Image recognition, Lesion alignment,
early melanoma diagnosis
BibRef
Boulahia, S.Y.[Said Yacine],
Benatia, M.A.[Mohamed Akram],
Bouzar, A.[Abderrahmane],
Att2ResNet: A deep attention-based approach for melanoma skin cancer
classification,
IJIST(32), No. 2, 2022, pp. 476-489.
DOI Link
2203
attention mechanism, classification, deep learning, melanoma, skin cancer
BibRef
Jiao, X.F.[Xiong-Fei],
Li, J.[Juan],
Zhao, Z.L.[Zhi-Lei],
Badami, B.[Benjamin],
An all-inclusive computer-aided melanoma diagnosis based on soft
computing,
IJIST(32), No. 4, 2022, pp. 1294-1306.
DOI Link
2207
decision trees, feature selection, fuzzy C-means,
image segmentation, melanoma, quantum fluid search optimization algorithm
BibRef
Filali, Y.[Youssef],
El Khoukhi, H.[Hasnae],
Sabri, M.A.[My Abdelouahed],
Aarab, A.[Abdellah],
Analysis and Classification of Skin Cancer Based on Deep Learning
Approach,
ISCV22(1-6)
IEEE DOI
2208
Deep learning, Computer architecture, Melanoma, Skin,
Real-time systems, Convolutional neural networks, Lesions,
Melanoma and non-melanoma skin cancer
BibRef
Gajera, H.K.[Himanshu K.],
Zaveri, M.A.[Mukesh A.],
Nayak, D.R.[Deepak Ranjan],
Patch-based local deep feature extraction for automated skin cancer
classification,
IJIST(32), No. 5, 2022, pp. 1774-1788.
DOI Link
2209
CNN, deep features, feature fusion, FNN, KPCA, skin cancer
BibRef
He, X.Y.[Xiao-Yu],
Wang, Y.[Yong],
Zhao, S.[Shuang],
Chen, X.[Xiang],
Co-Attention Fusion Network for Multimodal Skin Cancer Diagnosis,
PR(133), 2023, pp. 108990.
Elsevier DOI
2210
Skin cancer diagnosis, Convolutional neural networks,
Multimodal fusion, Attention mechanism
BibRef
Nguyen, H.T.[Hai Thanh],
Luong, H.H.[Huong Hoang],
Phan, P.T.[Phat Tan],
Nguyen, H.H.D.[Hung Huy Duc],
Ly, D.[Duong],
Phan, D.M.[Duc Minh],
Do, T.T.[Tin Trung],
HS-UNET-ID: An approach for human skin classification integrating
between UNET and improved dense convolutional network,
IJIST(32), No. 6, 2022, pp. 1832-1845.
DOI Link
2212
convolutional neural networks, human skin images,
skin cancer diseases, skin classification
BibRef
Nawaz, M.[Marriam],
Nazir, T.[Tahira],
Masood, M.[Momina],
Ali, F.[Farooq],
Khan, M.A.[Muhammad Attique],
Tariq, U.[Usman],
Sahar, N.[Naveera],
Damaševicius, R.[Robertas],
Melanoma segmentation: A framework of improved DenseNet77 and UNET
convolutional neural network,
IJIST(32), No. 6, 2022, pp. 2137-2153.
DOI Link
2212
deep learning, DenseNet, dermoscopy, melanoma, skin moles, UNET
BibRef
Hu, L.[Libing],
Zhang, Y.C.[Yong-Chun],
Chen, K.D.[Kai-Di],
Mobayen, S.[Saleh],
A computer-aided melanoma detection using deep learning and an
improved African vulture optimization algorithm,
IJIST(32), No. 6, 2022, pp. 2002-2016.
DOI Link
2212
computer-aided diagnosis, convolutional neural network,
improved African vulture optimization algorithm, melanoma,
SIIM-ISIC melanoma dataset
BibRef
Nowara, E.[Ewa],
Mcduff, D.[Daniel],
Sabharwal, A.[Ashutosh],
Veeraraghavan, A.[Ashok],
Seeing Beneath the Skin with Computational Photography,
CACM(65), No. 12, December 2022, pp. 90-100.
DOI Link
2212
BibRef
Zhou, H.Y.[Hong-Yu],
Lu, C.[Chixiang],
Wang, L.S.[Lian-Sheng],
Yu, Y.Z.[Yi-Zhou],
GraVIS: Grouping Augmented Views From Independent Sources for
Dermatology Analysis,
MedImg(41), No. 12, December 2022, pp. 3498-3508.
IEEE DOI
2212
Dermatology, Training, Self-supervised learning,
Biomedical imaging, Task analysis, Image representation,
self-supervised pre-training
BibRef
Kumar, V.A.[V. Akash],
Mishra, V.[Vijaya],
Arora, M.[Monika],
Deep Learning-Based Classification of Malignant and Benign Cells in
Dermatoscopic Images via Transfer Learning Approach,
IJIG(22), No. 5 2022, pp. 2250041.
DOI Link
2212
BibRef
Zhang, D.[Dong],
Yang, J.[Jing],
Du, S.[Shaoyi],
Han, H.C.[Hong-Cheng],
Ge, Y.Y.[Yu-Yan],
Zhu, L.F.[Long-Fei],
Li, C.[Ce],
Xu, M.[Meifeng],
Zheng, N.N.[Nan-Ning],
Coarse-to-fine feature representation based on deformable partition
attention for melanoma identification,
PR(136), 2023, pp. 109247.
Elsevier DOI
2301
Histopathological image, Melanoma identification,
Deformable convolution, Attention mechanism, Deep learning
BibRef
Parajuli, M.[Madan],
Shaban, M.[Mohamed],
Phung, T.L.[Thuy L.],
Automated differentiation of skin melanocytes from keratinocytes in
high-resolution histopathology images using a weakly-supervised
deep-learning framework,
IJIST(33), No. 1, 2023, pp. 262-275.
DOI Link
2301
artificial intelligence, deep-machine learning,
digital pathology, keratinocytes, melanocytes
BibRef
Ain, Q.U.[Qurrat Ul],
Al-Sahaf, H.[Harith],
Xue, B.[Bing],
Zhang, M.J.[Meng-Jie],
Automatically Diagnosing Skin Cancers From Multimodality Images Using
Two-Stage Genetic Programming,
Cyber(53), No. 5, May 2023, pp. 2727-2740.
IEEE DOI
2305
Feature extraction, Skin, Image color analysis, Lesions, Contracts,
Wavelet analysis, Visualization, Feature construction,
skin cancer images
BibRef
Ding, Q.[Qi],
Razmjooy, N.[Navid],
An optimal diagnosis system for melanoma dermoscopy images based on
enhanced design of horse herd optimizer,
IJIST(33), No. 3, 2023, pp. 1092-1107.
DOI Link
2305
dermoscopy, diagnosis, enhanced horse herd optimization algorithm,
SIIM-ISIC dataset
BibRef
Gayatri, E.[Erapaneni],
Aarthy, S.L.,
Challenges and Imperatives of Deep Learning Approaches for Detection of
Melanoma: A Review,
IJIG(23), No. 3 2023, pp. 2240012.
DOI Link
2306
BibRef
Bian, X.F.[Xiao-Fei],
Pan, H.W.[Hai-Wei],
Zhang, K.[Kejia],
Liu, P.[Peng],
Chen, C.L.[Chun-Ling],
Malignant melanoma dermoscopy image classification method based on
multi-modal medical features,
IET-IPR(17), No. 9, 2023, pp. 2611-2627.
DOI Link
2307
image classification, feature extraction, medical image processing
BibRef
Pérez, E.[Eduardo],
Reyes, Ó.[Óscar],
Performing Melanoma Diagnosis by an Effective Multi-view Convolutional
Network Architecture,
IJCV(131), No. 1, January 2023, pp. 3094-3117.
Springer DOI
2310
BibRef
Goceri, E.[Evgin],
Comparison of the impacts of dermoscopy image augmentation methods on
skin cancer classification and a new augmentation method with wavelet
packets,
IJIST(33), No. 5, 2023, pp. 1727-1744.
DOI Link
2310
deep learning, dermoscopy image, image augmentation,
skin lesion, wavelet packet transform
BibRef
Renith, G.,
Senthilselvi, A.,
An efficient skin cancer detection and classification using Improved
Adaboost Aphid-Ant Mutualism model,
IJIST(33), No. 6, 2023, pp. 1957-1972.
DOI Link
2311
AlexNet architecture, Aphid-Ant Mutualism, detection,
Improved Adaboost, pre-processing, skin cancer
BibRef
Söylemez, Ö.F.[Ömer Faruk],
Forward selection-based ensemble of deep neural networks for melanoma
classification in dermoscopy images,
IJIST(33), No. 6, 2023, pp. 1929-1943.
DOI Link
2311
deep learning, ensembling, forward selection, melanoma, skin cancer
BibRef
Sukanya, S.T.,
Jerine, S.,
Deep Learning-Based Melanoma Detection with Optimized Features via
Hybrid Algorithm,
IJIG(23), No. 6 2023, pp. 2350056.
DOI Link
2312
BibRef
Vidhyalakshmi, A.M.,
Kanchana, M.,
Skin cancer classification using improved transfer learning
model-based random forest classifier and golden search optimization,
IJIST(34), No. 1, 2024, pp. e22971.
DOI Link
2401
convolutional neural network (CNN),
Golden search optimization (GSO) algorithm, transfer learning (TL)
BibRef
Kaur, K.[Komalpreet],
Kaur, A.[Amanpreet],
In silico, in vitro, and in vivo validation of a microwave imaging
system using a low-profile Ultra Wide Band Archimedean spiral antenna
to detect skin cancer,
IJIST(34), No. 1, 2024, pp. e23016.
DOI Link
2401
beamforming algorithm, in vitro, in vivo, microwave imaging,
skin cancer, UWB ASMA
BibRef
Enturi, B.K.M.[B. Krishna Manash],
Suhasini, A.,
Satyala, N.[Narayana],
Optimized Deep CNN with Deviation Relevance-based LBP for Skin Cancer
Detection: Hybrid Metaheuristic Enabled Feature Selection,
IJIG(24), No. 2, March 2024, pp. 2450023.
DOI Link
2404
BibRef
Ahmed, N.[Noor],
Xin, T.[Tan],
Lizhuang, M.[Ma],
MSPAN: Multi-scale pyramid attention network for efficient skin
cancer lesion segmentation,
IET-IPR(18), No. 7, 2024, pp. 1667-1680.
DOI Link
2405
biomedical imaging, cancer, convolution,
convolutional neural nets
BibRef
Tang, P.[Peng],
Yan, X.T.[Xin-Tong],
Nan, Y.[Yang],
Hu, X.B.[Xia-Bin],
Menze, B.H.[Bjoern H.],
Krammer, S.[Sebastian],
Lasser, T.[Tobias],
Joint-individual fusion structure with fusion attention module for
multi-modal skin cancer classification,
PR(154), 2024, pp. 110604.
Elsevier DOI
2406
Skin cancer classification, Joint-individual fusion structure,
Multi-modal fusion attention, Dermatological image and metadata
BibRef
Imran, M.[Muhammad],
Akram, M.U.[Muhammad Usman],
Tiwana, M.I.[Mohsin Islam],
Salam, A.A.[Anum Abdul],
Hassan, T.[Taimur],
Greco, D.[Danilo],
Two-dimensional hybrid incremental learning (2DHIL) framework for
semantic segmentation of skin tissues,
IVC(148), 2024, pp. 105147.
Elsevier DOI
2407
BibRef
Earlier: A1, A2, A3, A4, A6, Only:
IVC(148), 2024, pp. 105098.
2407
BibRef
And:
Erratum:
IVC(148), 2024, pp. 105148.
Elsevier DOI
2407
Note -- erratum seems to partly correct author list in first version.
AI, Incremental semantic segmentation, Incremental learning,
Continual learning, Knowledge distillation, Computational histopathology
BibRef
Li, F.[Feng],
Li, M.[Min],
Zuo, E.[Enguang],
Chen, C.[Chen],
Chen, C.[Cheng],
Lv, X.Y.[Xiao-Yi],
Self-contrastive Feature Guidance Based Multidimensional
Collaborative Network of metadata and image features for skin disease
classification,
PR(156), 2024, pp. 110742.
Elsevier DOI
2408
Multimodal fusion, Multidimensional synergy, Contrast learning,
Feature refinement
BibRef
Zhao, G.Z.[Guang-Zhe],
Zhang, C.[Chen],
Wang, X.P.[Xue-Ping],
Lin, B.[Benwang],
Yan, F.H.[Fei-Hu],
PMANet: Progressive multi-stage attention networks for skin disease
classification,
IVC(149), 2024, pp. 105166.
Elsevier DOI
2408
Skin disease classification, Progressive networks, Attention mechanism
BibRef
Gu, X.Y.[Xin-Yu],
Zhang, X.X.[Xiao-Xu],
VPDet: Refined region CNN for hair follicle recognition in arbitrary
angle,
IET-IPR(18), No. 12, 2024, pp. 3156-3167.
DOI Link
2411
image recognition, object detection
BibRef
Meedeniya, D.[Dulani],
de Silva, S.[Senuri],
Gamage, L.[Lahiru],
Isuranga, U.[Uditha],
Skin cancer identification utilizing deep learning: A survey,
IET-IPR(18), No. 13, 2024, pp. 3731-3749.
DOI Link
2411
image classification, image segmentation, reviews, skin
BibRef
Hernández-Pérez, C.[Carlos],
Jimenez-Martin, L.[Lauren],
Vilaplana, V.[Veronica],
Bridging Domains in Melanoma Diagnostics: Predicting BRAF Mutations
and Sentinel Lymph Node Positivity with Attention-Based Models in
Histological Images,
DEF-AI-MIA24(5132-5140)
IEEE DOI
2410
Adaptation models, Pathology, Analytical models,
Biological system modeling, Supervised learning, Melanoma, Transformers
BibRef
Gonzalez-Jimenez, A.[Alvaro],
Lionetti, S.[Simone],
Pouly, M.[Marc],
Navarini, A.A.[Alexander A.],
SANO: Score-based Diffusion Model for Anomaly Localization in
Dermatology,
VAND23(2988-2994)
IEEE DOI
2309
BibRef
Yan, S.Y.[Si-Yuan],
Yu, Z.[Zhen],
Zhang, X.[Xuelin],
Mahapatra, D.[Dwarikanath],
Chandra, S.S.[Shekhar S.],
Janda, M.[Monika],
Soyer, P.[Peter],
Ge, Z.Y.[Zong-Yuan],
Towards Trustable Skin Cancer Diagnosis via Rewriting Model's
Decision,
CVPR23(11568-11577)
IEEE DOI
2309
BibRef
Grullon, S.[Sean],
Spurrier, V.[Vaughn],
Zhao, J.Y.[Jia-Yi],
Chivers, C.[Corey],
Jiang, Y.[Yang],
Motaparthi, K.[Kiran],
Lee, J.[Jason],
Bonham, M.[Michael],
Ianni, J.[Julianna],
Using Whole Slide Image Representations from Self-supervised
Contrastive Learning for Melanoma Concordance Regression,
MIA-COVID19D22(442-456).
Springer DOI
2304
BibRef
Jaworek-Korjakowska, J.[Joanna],
Wojcicka, A.[Anna],
Kucharski, D.[Dariusz],
Brodzicki, A.[Andrzej],
Kendrick, C.[Connah],
Cassidy, B.[Bill],
Yap, M.H.[Moi Hoon],
Skin_hair Dataset: Setting the Benchmark for Effective Hair Inpainting
Methods for Improving the Image Quality of Dermoscopic Images,
ISIC22(167-184).
Springer DOI
2304
BibRef
Du, S.[Siyi],
Hers, B.[Ben],
Bayasi, N.[Nourhan],
Hamarneh, G.[Ghassan],
Garbi, R.[Rafeef],
Fairdisco: Fairer AI in Dermatology via Disentanglement Contrastive
Learning,
ISIC22(185-202).
Springer DOI
2304
BibRef
Li, L.F.[Ling-Fang],
Qi, X.Y.[Xue-Ying],
Wang, X.[Xu],
Hu, W.J.[Wei-Jian],
Du, Y.X.[Yong-Xing],
Li, B.S.[Bao-Shan],
WAMF: A Weighted Adaptive Multi-convolution Fusion Scheme for
Dermoscopic Image Recognition,
ICIVC22(561-572)
IEEE DOI
2301
Training, Adaptation models, Image recognition, Adaptive systems,
Computational modeling, Transfer learning, image recognition
BibRef
del Amor, R.[Rocío],
Colomer, A.[Adrián],
Morales, S.[Sandra],
Pulgarín-Ospina, C.[Cristian],
Terradez, L.[Liria],
Aneiros-Fernandez, J.[Jose],
Naranjo, V.[Valery],
A Self-Contrastive Learning Framework for Skin Cancer Detection Using
Histological Images,
ICIP22(2291-2295)
IEEE DOI
2211
Pathology, Databases, Annotations, Manuals, Feature extraction,
Artificial intelligence, Neoplasms, Self-training,
Digital pathology
BibRef
Göçeri, E.[Evgin],
Impact of Deep Learning and Smartphone Technologies in Dermatology:
Automated Diagnosis,
IPTA20(1-6)
IEEE DOI
2206
Learning systems, Image processing, Dermatology, Skin, Lesions,
Medical diagnostic imaging, Diseases, automated diagnosis, skin diseases
BibRef
Casado-García, A.[Angela],
Agirresarobe, A.[Aitor],
Miranda-Apodaca, J.[Jon],
Heras, J.[Jónathan],
Pérez-López, U.[Usue],
Deep Detection Models for Measuring Epidermal Bladder Cells,
IbPRIA22(131-142).
Springer DOI
2205
BibRef
Alves, B.[Beatriz],
Barata, C.[Catarina],
Marques, J.S.[Jorge S.],
Diagnosis of Skin Cancer Using Hierarchical Neural Networks and
Metadata,
IbPRIA22(69-80).
Springer DOI
2205
BibRef
Nau, M.A.[Merlin A.],
Schiffers, F.[Florian],
Li, Y.H.[Yun-Hao],
Xu, B.J.[Bing-Jie],
Maier, A.[Andreas],
Tumblin, J.[Jack],
Walton, M.[Marc],
Katsaggelos, A.K.[Aggelos K.],
Willomitzer, F.[Florian],
Cossairt, O.[Oliver],
Skinscan: Low-Cost 3D-Scanning for Dermatologic Diagnosis and
Documentation,
ICIP21(2918-2922)
IEEE DOI
2201
Photography, Image processing, Skin, Software, Mobile handsets,
Topographic Imaging, Three-Dimensional Imaging, Dermatologic Imaging
BibRef
Alche, M.N.[Miguel Nehmad],
Acevedo, D.[Daniel],
Mejail, M.[Marta],
EfficientARL: improving skin cancer diagnoses by combining
lightweight attention on EfficientNet,
CVAMD21(3347-3353)
IEEE DOI
2112
Image color analysis, Malignant tumors,
Computational modeling, Melanoma, Skin
BibRef
Rao, A.[Adrit],
Park, J.[Jongchan],
Woo, S.[Sanghyun],
Lee, J.Y.[Joon-Young],
Aalami, O.[Oliver],
Studying the Effects of Self-Attention for Medical Image Analysis,
CVAMD21(3409-3418)
IEEE DOI
2112
Visualization, Image analysis, Melanoma,
Medical services, Convolutional neural networks
BibRef
Sankarapandian, S.[Sivaramakrishnan],
Kohn, S.[Saul],
Spurrier, V.[Vaughn],
Grullon, S.[Sean],
Soans, R.E.[Rajath E.],
Ayyagari, K.D.[Kameswari D.],
Chamarthi, R.V.[Ramachandra V.],
Motaparthi, K.[Kiran],
Lee, J.B.[Jason B.],
Shon, W.W.[Won-Woo],
Bonham, M.[Michael],
Ianni, J.D.[Julianna D.],
A Pathology Deep Learning System Capable of Triage of Melanoma
Specimens Utilizing Dermatopathologist Consensus as Ground Truth *,
CDPath21(629-638)
IEEE DOI
2112
Deep learning, Pathology, Sensitivity, Melanoma, Tools, Routing
BibRef
Stieler, F.[Fabian],
Rabe, F.[Fabian],
Bauer, B.[Bernhard],
Towards Domain-Specific Explainable AI: Model Interpretation of a
Skin Image Classifier using a Human Approach,
ISIC21(1802-1809)
IEEE DOI
2109
Deep learning, Image analysis,
Computational modeling, Skin
BibRef
Frasca, M.[Maria],
Nappi, M.[Michele],
Risi, M.[Michele],
Tortora, G.[Genoveffa],
Citarella, A.A.[Alessia Auriemma],
A Comparison of Neural Network Approaches for Melanoma Classification,
ICPR21(2110-2117)
IEEE DOI
2105
Deep learning, Sensitivity, Neural networks, Melanoma,
Medical services, Manuals, Sensitivity and specificity
BibRef
Xue, X.[Xi],
Kamata, S.I.[Sei-Ichiro],
Luo, D.M.[Da-Ming],
Skin Lesion Classification Using Weakly-supervised Fine-grained
Method,
ICPR21(9083-9090)
IEEE DOI
2105
Regulators, Neural networks, Melanoma, Feature extraction, Skin,
Lesions
BibRef
Mahbod, A.[Amirreza],
Schaefer, G.[Gerald],
Wang, C.L.[Chun-Liang],
Ecker, R.[Rupert],
Dorffner, G.[Georg],
Ellinger, I.[Isabella],
Investigating and Exploiting Image Resolution for Transfer
Learning-based Skin Lesion Classification,
ICPR21(4047-4053)
IEEE DOI
2105
Visualization, Image resolution, Transfer learning, Melanoma,
Receivers, Skin, Classification algorithms, Dermatology, skin cancer,
transfer learning
BibRef
Gupta, K.[Krishnam],
Jaiprasad,
Krishnan, M.[Monu],
Narayanan, A.[Ajit],
Narayan, N.S.[Nikhil S.],
Dual Stream Network with Selective Optimization for Skin Disease
Recognition in Consumer Grade Images,
ICPR21(5262-5269)
IEEE DOI
2105
Image segmentation, Lighting, Streaming media,
Network architecture, Skin, Classification algorithms,
dual stream deep networks
BibRef
Grove, R.,
Green, R.,
Melanoma and Nevi Classification using Convolution Neural Networks,
IVCNZ20(1-6)
IEEE DOI
2012
Training, Convolution, Neural networks, Focusing, Melanoma, Lesions,
Testing, melanoma, ResNet50, identification, classification
BibRef
Sabri, M.A.,
Filali, Y.,
El Khoukhi, H.,
Aarab, A.,
Skin Cancer Diagnosis Using an Improved Ensemble Machine Learning
model,
ISCV20(1-5)
IEEE DOI
2011
cancer, feature extraction, image classification,
learning (artificial intelligence), medical image processing,
ensemble learning.
BibRef
Bakkouri, I.[Ibtissam],
Afdel, K.[Karim],
Dermonet: A Computer-aided Diagnosis System for Dermoscopic Disease
Recognition,
ICISP20(170-177).
Springer DOI
2009
BibRef
Bagchi, S.,
Banerjee, A.,
Bathula, D.R.,
Learning A Meta-Ensemble Technique For Skin Lesion Classification And
Novel Class Detection,
ISIC20(3221-3228)
IEEE DOI
2008
Stacking, Training, Skin, Task analysis, Lesions, Image color analysis, Melanoma
BibRef
Coppola, D.,
Lee, H.K.,
Guan, C.,
Interpreting mechanisms of prediction for skin cancer diagnosis using
multi-task learning,
ISIC20(3162-3171)
IEEE DOI
2008
Task analysis, Lesions, Logic gates, Skin, Machine learning, Melanoma
BibRef
Singh, N.,
Lee, K.,
Coz, D.,
Angermueller, C.,
Huang, S.,
Loh, A.,
Liu, Y.,
Agreement Between Saliency Maps and Human-Labeled Regions of
Interest: Applications to Skin Disease Classification,
ISIC20(3172-3181)
IEEE DOI
2008
Skin, Diseases, Google, Analytical models, Dermatology, Data models, Image segmentation
BibRef
Pacheco, A.G.C.,
Sastry, C.S.,
Trappenberg, T.,
Oore, S.,
Krohling, R.A.,
On Out-of-Distribution Detection Algorithms with Deep Neural Skin
Cancer Classifiers,
ISIC20(3152-3161)
IEEE DOI
2008
Training, Melanoma, Neural networks, Detection algorithms, Dogs, Skin
BibRef
Adegun, A.[Adekanmi],
Viriri, S.[Serestina],
Deep Convolutional Network-based Framework for Melanoma Lesion
Detection and Segmentation,
ACIVS20(51-62).
Springer DOI
2003
BibRef
Dogvanich, A.,
Mamaev, N.,
Krylov, A.,
Makhneva, N.,
Dermatological Image Denoising Using Adaptive Henlm Method,
PTVSBB19(47-52).
DOI Link
1912
BibRef
Peng, J.,
Gao, R.,
Nguyen, L.,
Liang, Y.,
Thng, S.,
Lin, Z.,
Classification of Non-Tumorous Facial Pigmentation Disorders Using
Improved Smote and Transfer Learning,
ICIP19(220-224)
IEEE DOI
1910
improved SMOTE, facial pigmentation disorders,
biomedical images analysis and classification, transfer learning
BibRef
Neher, H.[Helmut],
Arlette, J.[John],
Wong, A.[Alexander],
Discovery Radiomics for Detection of Severely Atypical Melanocytic
Lesions (SAML) from Skin Imaging via Deep Residual Group Convolutional
Radiomic Sequencer,
ICIAR19(II:307-315).
Springer DOI
1909
BibRef
Salih, O.[Omran],
Viriri, S.[Serestina],
Skin Cancer Segmentation Using a Unified Markov Random Field,
ISVC18(25-33).
Springer DOI
1811
BibRef
Elmogy, M.[Mohammed],
García-Zapirain, B.[Begoña],
Elmaghraby, A.S.[Adel S.],
Ei-Baz, A.[Ayman],
An Automated Classification Framework for Pressure Ulcer Tissues
Based on 3D Convolutional Neural Network,
ICPR18(2356-2361)
IEEE DOI
1812
Image segmentation,
Image color analysis, Solid modeling, Skin, Kernel, Feature extraction
BibRef
Elmogy, M.[Mohammed],
García-Zapirain, B.[Begoña],
Burns, C.[Connor],
Elmaghraby, A.S.[Adel S.],
Ei-Baz, A.[Ayman],
Tissues Classification for Pressure Ulcer Images Based on 3D
Convolutional Neural Network,
ICIP18(3139-3143)
IEEE DOI
1809
Image segmentation, Kernel,
Image color analysis, Biological system modeling, Solid modeling,
Linear Combinations of Discrete Gaussians (LCDG)
BibRef
Coronado, R.[Ricardo],
Ocsa, A.[Alexander],
Quispe, O.[Oscar],
Non-dermatoscopic Image Analysis for the Recognition of Malignant Skin
Diseases with Convolutional Neural Network and Autoencoders,
CIARP17(160-167).
Springer DOI
1802
BibRef
Gu, Y.,
Zhou, J.,
Qian, B.,
Melanoma Detection Based on Mahalanobis Distance Learning and
Constrained Graph Regularized Nonnegative Matrix Factorization,
WACV17(797-805)
IEEE DOI
1609
Feature extraction, Linear programming, Malignant tumors,
Manifolds, Matrix decomposition, Skin, Skin, cancer
BibRef
Barata, C.[Catarina],
Figueiredo, M.A.T.[Mário A. T.],
Celebi, M.E.[M. Emre],
Marques, J.S.[Jorge S.],
Local Features Applied to Dermoscopy Images:
Bag-of-Features versus Sparse Coding,
IbPRIA17(528-536).
Springer DOI
1706
BibRef
Alarifi, J.S.[Jhan S.],
Goyal, M.[Manu],
Davison, A.K.[Adrian K.],
Dancey, D.[Darren],
Khan, R.[Rabia],
Yap, M.H.[Moi Hoon],
Facial Skin Classification Using Convolutional Neural Networks,
ICIAR17(479-485).
Springer DOI
1706
BibRef
Cho, D.S.[Daniel S.],
Khalvati, F.[Farzad],
Clausi, D.A.[David A.],
Wong, A.[Alexander],
A Machine Learning-Driven Approach to Computational Physiological
Modeling of Skin Cancer,
ICIAR17(79-86).
Springer DOI
1706
BibRef
Yao, T.T.[Ting-Ting],
Wang, Z.Y.[Zhi-Yong],
Xie, Z.[Zhao],
Gao, J.[Jun],
Feng, D.D.[David Dagan],
A Multiview Joint Sparse Representation with Discriminative
Dictionary for Melanoma Detection,
DICTA16(1-6)
IEEE DOI
1701
Dictionaries
BibRef
Bernart, E.,
Scharcanski, J.,
Bampi, S.,
Segmentation and classification of melanocytic skin lesions using
local and contextual features,
ICIP16(2633-2637)
IEEE DOI
1610
Cancer
BibRef
Mete, M.,
Sirakov, N.M.,
Griffin, J.,
Menter, A.,
A novel classification system for dysplastic nevus and malignant
melanoma,
ICIP16(3414-3418)
IEEE DOI
1610
Lesions
BibRef
Majtner, T.[Tomáš],
Yildirim-Yayilgan, S.[Sule],
Hardeberg, J.Y.[Jon Yngve],
Efficient Melanoma Detection Using Texture-Based RSurf Features,
ICIAR16(30-37).
Springer DOI
1608
BibRef
Akaho, R.[Rina],
Hirose, M.[Misa],
Tsumura, N.[Norimichi],
Nonlinear Estimation of Chromophore Concentrations and Shading from
Hyperspectral Images,
ICISP16(101-108).
WWW Link.
1606
melanin, oxy-hemoglobin, deoxy-hemoglobin and shading.
BibRef
Afifi, S.[Shereen],
Gholam Hosseini, H.[Hamid],
Sinha, R.[Roopak],
Hardware Acceleration of SVM-Based Classifier for Melanoma Images,
MCBMIIA15(235-245).
Springer DOI
1603
BibRef
Saleh, F.S.,
Azmi, R.,
Automated lesion border detection of dermoscopy images using spectral
clustering,
IPRIA15(1-6)
IEEE DOI
1603
cancer
BibRef
Hames, S.C.,
Ardigo, M.,
Soyer, H.P.,
Bradley, A.P.,
Prow, T.W.,
Anatomical Skin Segmentation in Reflectance Confocal Microscopy with
Weak Labels,
DICTA15(1-8)
IEEE DOI
1603
feature extraction
BibRef
Madooei, A.[Ali],
Drew, M.S.[Mark S.],
Detecting specular highlights in dermatological images,
ICIP15(4357-4360)
IEEE DOI
1512
DRM
BibRef
Codella, N.[Noel],
Cai, J.J.[Jun-Jie],
Abedini, M.[Mani],
Garnavi, R.[Rahil],
Halpern, A.[Alan],
Smith, J.R.[John R.],
Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in
Dermoscopy Images,
MLMI15(118-126).
Springer DOI
1511
BibRef
Kechichian, R.,
Gong, H.,
Revenu, M.,
Lezoray, O.,
Desvignes, M.,
New data model for graph-cut segmentation:
Application to automatic melanoma delineation,
ICIP14(892-896)
IEEE DOI
1502
Image color analysis
BibRef
Mete, M.[Mutlu],
Sirakov, N.M.[Nikolay Metodiev],
Optimal set of features for accurate skin cancer diagnosis,
ICIP14(2256-2260)
IEEE DOI
1502
Accuracy
BibRef
Kropidlowski, K.[Karol],
Kociolek, M.[Marcin],
Strzelecki, M.[Michal],
Czubinski, D.[Dariusz],
Model Based Approach for Melanoma Segmentation,
ICCVG14(347-355).
Springer DOI
1410
BibRef
Liu, Z.[Zhao],
Zerubia, J.B.[Josiane B.],
Melanin and Hemoglobin Identification for Skin Disease Analysis,
ACPR13(145-149)
IEEE DOI
1408
diseases
BibRef
Abuzaghleh, O.,
Barkana, B.D.,
Faezipour, M.,
SKINcure: A real time image analysis system to aid in the malignant
melanoma prevention and early detection,
Southwest14(85-88)
IEEE DOI
1406
cancer
BibRef
Hirose, M.[Misa],
Toyota, S.[Saori],
Tatsuzawa, Y.[Yuri],
Tsumura, N.[Norimichi],
Evaluating Visibility of Age Spot and Freckle Based on Simulated
Spectral Reflectance of Skin,
ICISP14(9-17).
Springer DOI
1406
BibRef
Vasconcelos, M.J.M.[Maria João M.],
Rosado, L.[Luís],
No-reference Blur Assessment of Dermatological Images Acquired via
Mobile Devices,
ICISP14(350-357).
Springer DOI
1406
BibRef
Seck, A.[Alassane],
Dee, H.[Hannah],
Tiddeman, B.[Bernard],
3D Facial Skin Texture Analysis Using Geometric Descriptors,
ICPR14(1126-1131)
IEEE DOI
1412
BibRef
Earlier:
Local Orientation Patterns for 3D Surface Texture Analysis of Normal
Maps: Application to Facial Skin Condition Classification,
ISVC13(I:572-581).
Springer DOI
1310
Feature extraction
BibRef
Barata, C.[Catarina],
Celebi, M.E.[M. Emre],
Marques, J.S.[Jorge S.],
Color Detection in Dermoscopy Images Based on Scarce Annotations,
IbPRIA15(309-316).
Springer DOI
1506
BibRef
Ruela, M.[Margarida],
Barata, C.[Catarina],
Marques, J.S.[Jorge S.],
What Is the Role of Color Symmetry in the Detection of Melanomas?,
ISVC13(I:1-10).
Springer DOI
1310
BibRef
Ruela, M.[Margarida],
Barata, C.[Catarina],
Mendonça, T.[Teresa],
Marques, J.S.[Jorge S.],
What Is the Role of Color in Dermoscopy Analysis?,
IbPRIA13(819-826).
Springer DOI
1307
BibRef
Ferreira, P.M.[Pedro M.],
Mendonça, T.[Teresa],
Rocha, P.[Paula],
A Wide Spread of Algorithms for Automatic Segmentation of Dermoscopic
Images,
IbPRIA13(592-599).
Springer DOI
1307
BibRef
Barata, C.[Catarina],
Marques, J.S.[Jorge S.],
Mendonça, T.[Teresa],
Bag-of-Features Classification Model for the Diagnose of Melanoma in
Dermoscopy Images Using Color and Texture Descriptors,
ICIAR13(547-555).
Springer DOI
1307
BibRef
Barata, C.[Catarina],
Marques, J.S.[Jorge S.],
Rozeira, J.[Jorge],
Evaluation of Color Based Keypoints and Features for the Classification
of Melanomas Using the Bag-of-Features Model,
ISVC13(I:40-49).
Springer DOI
1310
BibRef
Earlier:
The Role of Keypoint Sampling on the Classification of Melanomas in
Dermoscopy Images Using Bag-of-Features,
IbPRIA13(715-723).
Springer DOI
1307
BibRef
Mete, M.[Mutlu],
Ou, Y.L.[Ye-Lin],
Sirakov, N.M.[Nikolay Metodiev],
Skin Lesion Feature Vector Space with a Metric to Model Geometric
Structures of Malignancy for Classification,
IWCIA12(285-297).
Springer DOI
1211
BibRef
Gong, H.[Hao],
Desvignes, M.[Michel],
Hemoglobin and Melanin Quantification on Skin Images,
ICIAR12(II: 198-205).
Springer DOI
1206
BibRef
Garnavi, R.[Rahil],
Aldeen, M.[Mohammad],
Bailey, J.[James],
Classification of Melanoma Lesions Using Wavelet-Based Texture Analysis,
DICTA10(75-81).
IEEE DOI
1012
BibRef
Ali, M.[Mcheik],
Hadj, B.[Batatia],
Segmentation of oct skin images by classification of speckle
statistical parameters,
ICIP10(613-616).
IEEE DOI
1009
OCT: Optical Coherence Tompgraphy.
BibRef
Prigent, S.[Sylvain],
Descombes, X.[Xavier],
Zugaj, D.[Didier],
Petit, L.[Laurent],
Zerubia, J.B.[Josiane B.],
Multi-scale analysis of skin hyper-pigmentation evolution,
ICIP13(626-629)
IEEE DOI
1402
Diseases
BibRef
Prigent, S.[Sylvain],
Descombes, X.[Xavier],
Zugaj, D.[Didier],
Martel, P.[Philippe],
Zerubia, J.B.[Josiane B.],
Multi-spectral image analysis for skin pigmentation classification,
ICIP10(3641-3644).
IEEE DOI
1009
BibRef
Lu, J.[Juan],
Manton, J.H.[Jonathan H.],
Kazmierczak, E.[Ed],
Sinclair, R.[Rodney],
Erythema detection in digital skin images,
ICIP10(2545-2548).
IEEE DOI
1009
BibRef
Scheibe, P.[Patrick],
Wetzig, T.[Tino],
Kuska, J.P.[Jens-Peer],
Löffler, M.[Markus],
Simon, J.C.[Jan C.],
Paasch, U.[Uwe],
Braumann, U.D.[Ulf-Dietrich],
3D-Reconstruction of Basal Cell Carcinoma: A Proof-of-Principle Study,
WBIR10(25-36).
Springer DOI
1007
BibRef
Mendoza, C.S.[Carlos S.],
Serrano, C.[Carmen],
Acha, B.[Begona],
Scale invariant descriptors in pattern analysis of melanocytic lesions,
ICIP09(4193-4196).
IEEE DOI
0911
BibRef
Schaefer, G.,
Krawczyk, B.,
Celebi, M.E.,
Iyatomi, H.,
Melanoma Classification Using Dermoscopy Imaging and Ensemble
Learning,
ACPR13(386-390)
IEEE DOI
1408
biomedical optical imaging
BibRef
Bouhlel, N.[Nizar],
Hajjaji, S.[Salwa],
Sevestre, S.[Sylvie],
Laugier, P.[Pascal],
Texture analysis using Nakagami-MRF model: Preliminary results on
ultrasound images of primary choroidal melanomas,
ICIP09(4181-4184).
IEEE DOI
0911
BibRef
Chaudhry, M.A.[M. Ali],
Ashraf, R.[Robina],
Jafri, M.N.,
Akbar, M.,
Computer aided diagnosis of skin carcinomas based on textural
characteristics,
ICMV07(125-128).
IEEE DOI
0712
BibRef
Cho, T.S.[Taeg Sang],
Freeman, W.T.[William T],
Tsao, H.[Hensin],
A reliable skin mole localization scheme,
MMBIA07(1-8).
IEEE DOI
0710
BibRef
Xu, C.Z.[Cheng-Zhe],
Kim, I.[Intaek],
Kim, M.S.[Moon S.],
Poultry Skin Tumor Detection in Hyperspectral Reflectance Images by
Combining Classifiers,
ICIAR07(1289-1296).
Springer DOI
0708
BibRef
Aribisala, B.S.[Benjamin S.],
Claridge, E.[Ela],
A Border Irregularity Measure Using a Modified Conditional Entropy
Method as a Malignant Melanoma Predictor,
ICIAR05(914-921).
Springer DOI
0509
BibRef
Kontinen, J.[Jukka],
Röning, J.[Juha],
MacKie, R.M.[Rona M.],
Texture features in the classification of melanocytic lesions,
CIAP97(II: 453-460).
Springer DOI
9709
BibRef
Yamada, T.,
Saito, H.,
Ozawa, S.,
3d Reconstruction of Skin Surface from Image Sequence,
MVA98(xx-yy).
See also 3D Reconstruction of Book Surface Taken from Image Sequence with Handy Camera.
BibRef
9800
Roehrer, R.[Reinhard],
Ganster, H.[Harald],
Pinz, A.[Axel],
Feature Selection in Melanoma Recognition,
ICPR98(Vol II: 1668-1670).
IEEE DOI
9808
BibRef
Riech, M.[Marcel],
Roning, J.[Juha],
Registration of NEVI in Successive Skin Images for
Early Detection of Melanoma,
ICPR98(Vol I: 352-357).
IEEE DOI
9808
BibRef
Kutics, A.[Andrea],
Date, M.[Munehiro],
A new parallel method based on a genetic approach for determination and
classification of skin spots,
CIAP95(121-126).
Springer DOI
9509
BibRef
Ross, T.,
Handels, H.,
Kreusch, J.,
Busche, H.,
Wolf, H.H.,
Pöppl, S.J.,
Automatic classification of skin tumours with high resolution surface
profiles,
CAIP95(368-375).
Springer DOI
9509
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Skin Lesions, Wound Healing .