20.13.5 Medical Applications -- Skin Cancer, Melanoma

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.[Jinshan],
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.[Hongming], 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.[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.[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

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
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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
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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
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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
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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

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, Market research, 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, Pattern recognition 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, Pattern recognition, 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
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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
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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
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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).
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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).
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Kechichian, R., Gong, H., Revenu, M., Lezoray, O., Desvignes, M.,
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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).
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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],
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Vasconcelos, M.J.M.[Maria João M.], Rosado, L.[Luís],
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ICISP14(350-357).
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Seck, A.[Alassane], Dee, H.[Hannah], Tiddeman, B.[Bernard],
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ICPR14(1126-1131)
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Local Orientation Patterns for 3D Surface Texture Analysis of Normal Maps: Application to Facial Skin Condition Classification,
ISVC13(I:572-581).
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Ruela, M.[Margarida], Barata, C.[Catarina], Marques, J.S.[Jorge S.],
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Ruela, M.[Margarida], Barata, C.[Catarina], Mendonça, T.[Teresa], Marques, J.S.[Jorge S.],
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Ferreira, P.M.[Pedro M.], Mendonça, T.[Teresa], Rocha, P.[Paula],
A Wide Spread of Algorithms for Automatic Segmentation of Dermoscopic Images,
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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).
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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).
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The Role of Keypoint Sampling on the Classification of Melanomas in Dermoscopy Images Using Bag-of-Features,
IbPRIA13(715-723).
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Mete, M.[Mutlu], Ou, Y.L.[Ye-Lin], Sirakov, N.M.[Nikolay Metodiev],
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Gong, H.[Hao], Desvignes, M.[Michel],
Hemoglobin and Melanin Quantification on Skin Images,
ICIAR12(II: 198-205).
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Garnavi, R.[Rahil], Aldeen, M.[Mohammad], Bailey, J.[James],
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ICIP13(626-629)
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Diseases BibRef

Prigent, S.[Sylvain], Descombes, X.[Xavier], Zugaj, D.[Didier], Martel, P.[Philippe], Zerubia, J.B.[Josiane B.],
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ICIP10(2545-2548).
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WBIR10(25-36).
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Mendoza, C.S.[Carlos S.], Serrano, C.[Carmen], Acha, B.[Begona],
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ICIP09(4193-4196).
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Schaefer, G., Krawczyk, B., Celebi, M.E., Iyatomi, H.,
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ACPR13(386-390)
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biomedical optical imaging BibRef

Bouhlel, N.[Nizar], Hajjaji, S.[Salwa], Sevestre, S.[Sylvie], Laugier, P.[Pascal],
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ICMV07(125-128).
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MMBIA07(1-8).
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Kontinen, J.[Jukka], Röning, J.[Juha], MacKie, R.M.[Rona M.],
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CIAP97(II: 453-460).
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Yamada, T., Saito, H., Ozawa, S.,
3d Reconstruction of Skin Surface from Image Sequence,
MVA98(xx-yy).
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Riech, M.[Marcel], Roning, J.[Juha],
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Kutics, A.[Andrea], Date, M.[Munehiro],
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CIAP95(121-126).
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CAIP95(368-375).
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Skin Lesions, Wound Healing .