20.13 Medical Applications -- Cancer Diagnosis and Analysis

Chapter Contents (Back)
Cancer Detection. Tumor Detection. Medical, Applications.

Watanabe, S.[Sadakazu], and the CYBEST Group,
An automated apparatus for cancer prescreening: CYBEST,
CGIP(3), No. 4, December 1974, pp. 350-358.
WWW Link. 0501
BibRef

Moore, G.W.[G. William], Hutchins, G.M.[Grover M.], de la Monte, S.M.[Suzanne M.],
Lattice theory approach to metastatic disease patterns in autopsied human patients: Application to metastatic neuroblastoma,
PR(18), No. 2, 1985, pp. 91-102.
WWW Link. 0309
BibRef

Poulsen, R.S., Pedron, I.,
Region of Interest Finding in Reduced Resolution Color Imagery: Application to Cancer Cell Detection,
PR(28), No. 11, November 1995, pp. 1645-1655.
WWW Link. BibRef 9511

Zhu, Y.[Yan], Yan, Z.[Zhu],
Computerized tumor boundary detection using a Hopfield neural network,
MedImg(16), No. 1, February 1997, pp. 55-67.
IEEE Top Reference. 0205
BibRef

Clark, M.C., Hall, L.O., Goldgof, D.B., Velthuizen, R.P., Murtagh, F.R., Silbiger, M.S.,
Automatic Tumor Segmentation Using Knowledge-Based Techniques,
MedImg(17), No. 2, April 1998, pp. 187-201.
IEEE Top Reference. 9808
BibRef

Smallwood, R.H., Keshtkar, A., Wilkinson, B.A., Lee, J.A., Hamdy, F.C.,
Electrical impedance spectroscopy (EIS) in the urinary bladder: the effect of inflammation and edema on identification of malignancy,
MedImg(21), No. 6, June 2002, pp. 708-710.
IEEE Top Reference. 0208
BibRef

Liu, L., Bland, P.H., Williams, D.M., Schunck, B.G., Meyer, C.R.,
Application of robust sequential edge detection and linking to boundaries of low contrast lesions in medical images,
CVPR89(582-587).
IEEE DOI 0403
BibRef

Diaz, M.[Mireya], Rao, J. .S.I.[J. Sun-Il],
Non-parametric bootstrap ensembles for detection of tumor lesions,
PRL(28), No. 16, December 2007, pp. 2273-2283.
WWW Link. 0711
Statistical pattern recognition; Image analysis; Ensembles; Spatial correlation; Markov Random Fields; Unsupervised training BibRef

Gimi, B., Pathak, A.P., Ackerstaff, E., Glunde, K., Artemov, D., Bhujwalla, Z.M.,
Molecular Imaging of Cancer: Applications of Magnetic Resonance Methods,
PIEEE(93), No. 4, April 2005, pp. 784-799.
IEEE DOI 0504
BibRef

Thorne, S.H., Contag, C.H.,
Using in Vivo Bioluminescence Imaging to Shed Light on Cancer Biology,
PIEEE(93), No. 4, April 2005, pp. 750-762.
IEEE DOI 0504
BibRef

Zhou, B.[Bin], Xuan, J.H.[Jian-Hua], Wu, Q.R.[Qing-Rong], Wang, Y.[Yue],
3-D Deformation Guided On-Line Modification of Multi-leaf Collimators for Adaptive Radiation Therapy,
ICIAR08(xx-yy).
Springer DOI 0806
BibRef

Tommasi, T.[Tatiana], Orabona, F.[Francesco], Caputo, B.[Barbara],
Discriminative cue integration for medical image annotation,
PRL(29), No. 15, 1 November 2008, pp. 1996-2002.
WWW Link. 0811
Medical image annotation; Cue integration; Support vector machine See also Learning methods for melanoma recognition. BibRef

Kowalski, J.[Jeanne], Talbot, Jr., C.[Conover], Tsai, H.L.[Hua L.], Prasad, N.[Nijaguna], Umbricht, C.[Christopher], Zeiger, M.A.[Martha A.],
From ambiguities to insights in cancer diagnosis via query-based comparisons,
PR(42), No. 4, April 2009, pp. 575-580.
Elsevier DOI 0812
Gene expression; Query; Thyroid cancer BibRef

Tang, J.S.[Jin-Shan], Rangayyan, R.[Raj], Yao, J.H.[Jian-Hua], Yang, Y.Y.[Yong-Yi],
Digital image processing and pattern recognition techniques for the detection of cancer,
PR(42), No. 6, June 2009, pp. 1015-1016.
Elsevier DOI 0902
BibRef

Li, F., Zhou, X., Ma, J., Wong, S.T.C.,
Multiple Nuclei Tracking Using Integer Programming for Quantitative Cancer Cell Cycle Analysis,
MedImg(29), No. 1, January 2010, pp. 96-105.
IEEE DOI 1001
BibRef

Taheri, S.[Sima], Ong, S.H.[Sim Heng], Chong, V.F.H.[Vincent F.H.],
Level-set segmentation of brain tumors using a threshold-based speed function,
IVC(28), No. 1, Januray 2010, pp. 26-37.
Elsevier DOI 1001
BibRef
Earlier:
Threshold-based 3D Tumor Segmentation using Level Set (TSL),
WACV07(45-45).
IEEE DOI 0702
3D segmentation; Threshold; Level-set BibRef

Konukoglu, E., Clatz, O., Menze, B.H., Stieltjes, B., Weber, M.A., Mandonnet, E., Delingette, H., Ayache, N.J.,
Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations,
MedImg(29), No. 1, January 2010, pp. 77-95.
IEEE DOI 1001
BibRef

Rekik, I.[Islem], Allassonnière, S.[Stéphanie], Clatz, O.[Olivier], Geremia, E.[Ezequiel], Stretton, E.[Erin], Delingette, H.[Hervé], Ayache, N.J.[Nicholas J.],
Tumor growth parameters estimation and source localization from a unique time point: Application to low-grade gliomas,
CVIU(117), No. 3, March 2013, pp. 238-249.
Elsevier DOI 1302
Diffusivity ratio; Source estimation; Eikonal equation; Reaction-diffusion glioma growth modeling BibRef

Huang, P.W.[Po-Whei], Lai, Y.H.[Yan-Hao],
Effective segmentation and classification for HCC biopsy images,
PR(43), No. 4, April 2010, pp. 1550-1563.
Elsevier DOI 1002
HCC biopsy image; Morphological grayscale reconstruction; k-nearest neighbor; Support vector machine; Feature selection; Decision-graph BibRef

Worz, S., Sander, P., Pfannmoller, M., Rieker, R.J., Joos, S., Mechtersheimer, G., Boukamp, P., Lichter, P., Rohr, K.,
3D Geometry-Based Quantification of Colocalizations in Multichannel 3D Microscopy Images of Human Soft Tissue Tumors,
MedImg(29), No. 8, August 2010, pp. 1474-1484.
IEEE DOI 1008
BibRef

Johnson, J.P., Krupinski, E.A., Yan, M., Roehrig, H., Graham, A.R., Weinstein, R.S.,
Using a Visual Discrimination Model for the Detection of Compression Artifacts in Virtual Pathology Images,
MedImg(30), No. 2, February 2011, pp. 306-314.
IEEE DOI 1102
BibRef

Weibel, T.[Thomas], Daul, C.[Christian], Wolf, D.[Didier], Rösch, R.[Ronald], Guillemin, F.[François],
Graph based construction of textured large field of view mosaics for bladder cancer diagnosis,
PR(45), No. 12, December 2012, pp. 4138-4150.
Elsevier DOI 1208
Image mosaicing; Seamless panoramic stitching; Image registration; Bladder cancer; Endoscopy; Graph cuts; Higher order terms; Non-linear refinement BibRef

Sun, Z.L.[Zhan-Li], Zheng, C.H.[Chun-Hou], Gao, Q.W.[Qing-Wei], Zhang, J.[Jun], Zhang, D.X.[De-Xiang],
Tumor Classification Using Eigengene-Based Classifier Committee Learning Algorithm,
SPLetters(19), No. 8, August 2012, pp. 455-458.
IEEE DOI 1208
BibRef

Li, X.L.[Xiu-Li], Chen, X.J.[Xin-Jian], Yao, J.H.[Jian-Hua], Zhang, X.[Xing], Yang, F.[Fei], Tian, J.[Jian],
Automatic Renal Cortex Segmentation Using Implicit Shape Registration and Novel Multiple Surfaces Graph Search,
MedImg(31), No. 10, October 2012, pp. 1849-1860.
IEEE DOI 1210
BibRef
And: MedImg(31), No. 12, December 2012, pp. 2366.
IEEE DOI 1212
BibRef

Martel, S.,
Journey to the center of a tumor,
Spectrum(49), No. 10, October 2012, pp. 48-53.
IEEE DOI 1210
BibRef

Ozdemir, E., Gunduz-Demir, C.,
A Hybrid Classification Model for Digital Pathology Using Structural and Statistical Pattern Recognition,
MedImg(32), No. 2, February 2013, pp. 474-483.
IEEE DOI 1301
BibRef

Song, Q., Bai, J., Han, D.F., Bhatia, S., Sun, W., Rockey, W., Bayouth, J.E., Buatti, J.M., Wu, X.D.,
Optimal Co-Segmentation of Tumor in PET-CT Images With Context Information,
MedImg(32), No. 9, 2013, pp. 1685-1697.
IEEE DOI 1309
Context information BibRef

Ventola, G.M.[Giovanna Maria], Colaprico, A.[Antonio], d'Angelo, F.[Fulvio], Colantuoni, V.[Vittorio],
An Approach to Identify miRNA Associated with Cancer Altered Pathways,
PR-PS-BB13(399-408).
Springer DOI 1309
BibRef

Heckel, F., Meine, H., Moltz, J.H., Kuhnigk, J.M., Heverhagen, J.T., Kiessling, A., Buerke, B., Hahn, H.K.,
Segmentation-Based Partial Volume Correction for Volume Estimation of Solid Lesions in CT,
MedImg(33), No. 2, February 2014, pp. 462-480.
IEEE DOI 1403
cancer BibRef

Rajaguru, H.[Harikumar], Bojan, V.K.[Vinoth Kumar],
Performance analysis of EM, SVD, and SVM classifiers in classification of carcinogenic regions of medical images,
IJIST(24), No. 1, 2014, pp. 16-22.
DOI Link 1403
EM, SVD, SVM, performance measures, quality metrics BibRef

Rajaguru, H.[Harikumar], Ganesan, K.[Karthick], Bojan, V.K.[Vinoth Kumar],
Earlier detection of cancer regions from MR image features and SVM classifiers,
IJIST(26), No. 3, 2016, pp. 196-208.
DOI Link 1609
MR images, segmentation, texture features, SVM BibRef

Huang, H.[Hu], Tosun, A.B.[Akif Burak], Guo, J.[Jia], Chen, C.[Cheng], Wang, W.[Wei], Ozolek, J.A.[John A.], Rohde, G.K.[Gustavo K.],
Cancer diagnosis by nuclear morphometry using spatial information,
PRL(42), No. 1, 2014, pp. 115-121.
Elsevier DOI 1404
Set classification BibRef

Adcock, A.[Aaron], Rubin, D.[Daniel], Carlsson, G.[Gunnar],
Classification of hepatic lesions using the matching metric,
CVIU(121), No. 1, 2014, pp. 36-42.
Elsevier DOI 1404
Medical image processing BibRef

Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., Huisman, H.,
Computer-Aided Detection of Prostate Cancer in MRI,
MedImg(33), No. 5, May 2014, pp. 1083-1092.
IEEE DOI 1405
Biopsy BibRef

Mitra, S., Shankar, B.U.,
Integrating Radio Imaging With Gene Expressions Toward a Personalized Management of Cancer,
HMS(44), No. 5, October 2014, pp. 664-677.
IEEE DOI 1411
biomedical MRI BibRef

Gangeh, M.J., Sadeghi-Naini, A., Diu, M., Tadayyon, H., Kamel, M.S., Czarnota, G.J.,
Categorizing Extent of Tumor Cell Death Response to Cancer Therapy Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy,
MedImg(33), No. 6, June 2014, pp. 1390-1400.
IEEE DOI 1407
Biomedical imaging BibRef

McCann, M.T., Ozolek, J.A., Castro, C.A., Parvin, B., Kovacevic, J.,
Automated Histology Analysis: Opportunities for signal processing,
SPMag(32), No. 1, January 2015, pp. 78-87.
IEEE DOI 1502
cancer BibRef

Chang, H.[Hang], Parvin, B.[Bahram],
Classification of 3D Multicellular Organization in Phase Microscopy for High Throughput Screening of Therapeutic Targets,
WACV15(436-441)
IEEE DOI 1503
Breast cancer BibRef

Chang, H.[Hang], Zhou, Y.[Yin], Borowsky, A.[Alexander], Barner, K.[Kenneth], Spellman, P.T.[Paul T.], Parvin, B.[Bahram],
Stacked Predictive Sparse Decomposition for Classification of Histology Sections,
IJCV(113), No. 1, May 2015, pp. 3-18.
Springer DOI 1506
BibRef

Zhou, Y.[Yin], Chang, H.[Hang], Barner, K.[Kenneth], Spellman, P.T.[Paul T.], Parvin, B.[Bahram],
Classification of Histology Sections via Multispectral Convolutional Sparse Coding,
CVPR14(3081-3088)
IEEE DOI 1409
BibRef

Chang, H.[Hang], Zhou, Y.[Yin], Spellman, P.T.[Paul T.], Parvin, B.[Bahram],
Stacked Predictive Sparse Coding for Classification of Distinct Regions in Tumor Histopathology,
ICCV13(169-176)
IEEE DOI 1403
BibRef

Chang, H.[Hang], Borowsky, A.[Alexander], Spellman, P.T.[Paul T.], Parvin, B.[Bahram],
Classification of Tumor Histology via Morphometric Context,
CVPR13(2203-2210)
IEEE DOI 1309
BibRef

Ginsburg, S.B., Lee, G., Ali, S., Madabhushi, A.,
Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology,
MedImg(35), No. 1, January 2016, pp. 76-88.
IEEE DOI 1601
Diseases BibRef

Siegert, Y., Jiang, X., Krieg, V., Bartholomäus, S.,
Classification-Based Record Linkage With Pseudonymized Data for Epidemiological Cancer Registries,
MultMed(18), No. 10, October 2016, pp. 1929-1941.
IEEE DOI 1610
cancer BibRef

Li, J.D.[Jun-Dong], Adilmagambetov, A.[Aibek], Jabbar, M.S.M.[Mohomed Shazan Mohomed], Zaïane, O.R.[Osmar R.], Osornio-Vargas, A.[Alvaro], Wine, O.[Osnat],
On discovering co-location patterns in datasets: A case study of pollutants and child cancers,
GeoInfo(20), No. 4, October 2016, pp. 651-692.
Springer DOI 1610
BibRef

Wang, H.[He], Feng, Y.M.[Yuan-Ming], Sa, Y.[Yu], Lu, J.Q.[Jun Q.], Ding, J.H.[Jun-Hua], Zhang, J.[Jun], Hu, X.H.[Xin-Hua],
Pattern recognition and classification of two cancer cell lines by diffraction imaging at multiple pixel distances,
PR(61), No. 1, 2017, pp. 234-244.
Elsevier DOI 1705
Single-cell assay BibRef

Wong, K.C.L., Summers, R.M., Kebebew, E., Yao, J.,
Pancreatic Tumor Growth Prediction With Elastic-Growth Decomposition, Image-Derived Motion, and FDM-FEM Coupling,
MedImg(36), No. 1, January 2017, pp. 111-123.
IEEE DOI 1701
Biological system modeling BibRef

Li, L.[Laquan], Wang, J.[Jian], Lu, W.[Wei], Tan, S.[Shan],
Simultaneous tumor segmentation, image restoration, and blur kernel estimation in PET using multiple regularizations,
CVIU(155), No. 1, 2017, pp. 173-194.
Elsevier DOI 1702
Image restoration BibRef

Lapuyade-Lahorgue, J., Xue, J.H., Ruan, S.,
Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions,
IP(26), No. 7, July 2017, pp. 3187-3195.
IEEE DOI 1706
Bayes methods, Fuses, Hidden Markov models, Image segmentation, Magnetic resonance imaging, Probabilistic logic, Tumors, Bayesian inference, Data fusion, copulas, hidden Markov fields, multi-source images, tumor, segmentation BibRef

Guo, H., He, X., Liu, M., Zhang, Z., Hu, Z., Tian, J.,
Weight Multispectral Reconstruction Strategy for Enhanced Reconstruction Accuracy and Stability With Cerenkov Luminescence Tomography,
MedImg(36), No. 6, June 2017, pp. 1337-1346.
IEEE DOI 1706
Image reconstruction, Optical imaging, Optical scattering, Photonics, Tomography, Tumors, Cerenkov luminescence tomography, Weight multispectral reconstruction, inverse, problem BibRef

Carneiro, G., Peng, T., Bayer, C., Navab, N.,
Automatic Quantification of Tumour Hypoxia From Multi-Modal Microscopy Images Using Weakly-Supervised Learning Methods,
MedImg(36), No. 7, July 2017, pp. 1405-1417.
IEEE DOI 1707
Biomedical imaging, Cancer, Computational modeling, Manuals, Medical treatment, Training, Tumors, Microscopy, deep learning, high-order loss functions, structured output learning, weakly-supervised, training BibRef

Jia, Z., Huang, X., Chang, E.I.C., Xu, Y.,
Constrained Deep Weak Supervision for Histopathology Image Segmentation,
MedImg(36), No. 11, November 2017, pp. 2376-2388.
IEEE DOI 1711
Cancer, Neural networks, Prediction algorithms, Convolutional neural networks, BibRef


Zhu, X., Yao, J., Zhu, F., Huang, J.,
WSISA: Making Survival Prediction from Whole Slide Histopathological Images,
CVPR17(6855-6863)
IEEE DOI 1711
Cancer, Computational modeling, Feature extraction, Lungs, Training, Tumors BibRef

Zhang, Z., Xie, Y., Xing, F., McGough, M., Yang, L.,
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network,
CVPR17(3549-3557)
IEEE DOI 1711
Bladder, Cancer, Computational modeling, Medical diagnostic imaging, Visualization BibRef

Wang, C., Bu, H., Bao, J., Li, C.,
A Level Set Method for Gland Segmentation,
Microscopy17(865-873)
IEEE DOI 1709
Glands, Image segmentation, Level set, Machine learning, Pathology, Shape, Standards BibRef

Li, C., Gupta, S., Rana, S., Nguyen, V.[Vu], Venkatesh, S., Ashley, D., Livingston, T.,
Multiple adverse effects prediction in longitudinal cancer treatment,
ICPR16(3156-3161)
IEEE DOI 1705
Cancer, Chemotherapy, Correlation, Fatigue, Optimization, Predictive models, Symmetric matrices, adverse effects, cancer treatment, longitudinal prediction, multiple-output, regression BibRef

Stanitsas, P., Cherian, A., Li, X.[Xinyan], Truskinovsky, A., Morellas, V., Papanikolopoulos, N.,
Evaluation of feature descriptors for cancerous tissue recognition,
ICPR16(1490-1495)
IEEE DOI 1705
Cancer, Covariance matrices, Feature extraction, Geometry, Histograms, Image color analysis, Symmetric, matrices BibRef

Saha, B., Gupta, S., Phung, D., Venkatesh, S.,
Transfer learning for rare cancer problems via Discriminative Sparse Gaussian Graphical model,
ICPR16(537-542)
IEEE DOI 1705
Cancer, Cost function, Covariance matrices, Data models, Graphical models, Mathematical model, Training BibRef

Singh, V.R.,
Keynote speaker: Nano-cancer technology: New diagnostic and therapeutic devices,
IVPR17(1-1)
IEEE DOI 1704
Biographies;Ultrasonic variables measurement BibRef

Zhang, L.[Lei], Zhu, Y.[Ying],
CutPointVis: An Interactive Exploration Tool for Cancer Biomarker Cutpoint Optimization,
ISVC16(I: 55-64).
Springer DOI 1701
BibRef

Paul, A., Mukherjee, D.P.,
Gland segmentation from histology images using informative morphological scale space,
ICIP16(4121-4125)
IEEE DOI 1610
Cancer BibRef

Williams, E.,
The role of imaging in the detection, identification, and treatment of cancer,
AIPR15(1-6)
IEEE DOI 1605
biomedical imaging BibRef

Kourd Kaouther, E., Eddine Khelil, S., Hammoum, S.,
Study with RK4 ANOVA the location of the tumor at the smallest time for multi-images,
ICCVIA15(1-6)
IEEE DOI 1603
Gaussian distribution BibRef

Harai, Y., Tanaka, T.,
Automatic Diagnosis Support System Using Nuclear and Luminal Features,
DICTA15(1-8)
IEEE DOI 1603
cancer BibRef

Carneiro, G.[Gustavo], Peng, T.Y.[Ting-Ying], Bayer, C.[Christine], Navab, N.[Nassir],
Automatic detection of necrosis, normoxia and hypoxia in tumors from multimodal cytological images,
ICIP15(2429-2433)
IEEE DOI 1512
Classifier Combination BibRef

Sinha, D., Garain, U., Bandyopadhyay, S.,
Event extraction from cancer genetics literature,
ICAPR15(1-6)
IEEE DOI 1511
biology BibRef

Carneiro, G.[Gustavo], Peng, T.Y.[Ting-Ying], Bayer, C.[Christine], Navab, N.[Nassir],
Weakly-Supervised Structured Output Learning with Flexible and Latent Graphs Using High-Order Loss Functions,
ICCV15(648-656)
IEEE DOI 1602
BibRef
Earlier:
Flexible and Latent Structured Output Learning, Application to Histology,
MLMI15(220-228).
Springer DOI 1511
Tumors lack oxygen supply. BibRef

Liu, X.[Xiao], Shi, J.[Jun], Zhang, Q.[Qi],
Tumor Classification by Deep Polynomial Network and Multiple Kernel Learning on Small Ultrasound Image Dataset,
MLMI15(313-320).
Springer DOI 1511
BibRef

Lyksborg, M.[Mark], Puonti, O.[Oula], Agn, M.[Mikael], Larsen, R.[Rasmus],
An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation,
SCIA15(201-211).
Springer DOI 1506
BibRef

Albalooshi, F., Smith, S., Diskin, Y., Sidike, P., Asari, V.,
Automatic segmentation of carcinoma in radiographs,
AIPR14(1-6)
IEEE DOI 1504
biological tissues BibRef

Pak, F.[Fatemeh], Kanan, H.R.[Hamidreza Rashidy], Alikhassi, A.[Afsaneh],
Improvement of Benign and Malignant Probability Detection Based on Non-subsample Contourlet Transform and Super-resolution,
ICPR14(895-899)
IEEE DOI 1412
Accuracy BibRef

Nalepa, J.[Jakub], Szymanek, J.[Janusz], Hayball, M.P.[Michael P.], Brown, S.J.[Stephen J.], Ganeshan, B.[Balaji], Miles, K.[Kenneth],
Texture Analysis for Identifying Heterogeneity in Medical Images,
ICCVG14(446-453).
Springer DOI 1410
general for CT, MRI or PET. Tumors. BibRef

Esteves, T.[Tiago], Oliveira, M.J.[Maria José], Quelhas, P.[Pedro],
Cancer Cell Detection and Tracking Based on Local Interest Point Detectors,
ICIAR13(434-441).
Springer DOI 1307
BibRef
And:
Cancer Cell Detection and Morphology Analysis Based on Local Interest Point Detectors,
IbPRIA13(624-631).
Springer DOI 1307
BibRef

Pham, T.D.[Tuan D.], Ichikawa, K.[Kazuhisa],
Characterization of Cancer and Normal Intracellular Images by the Power Law of a Fuzzy Partition Functional,
ICIAR13(597-604).
Springer DOI 1307
BibRef

Wu, W.K.H.[William K.H.], Chung, A.C.S.[Albert C.S.], Lam, H.H.N.[Henry H.N.],
Multi-resolution LC-MS images alignment using dynamic time warping and Kullback-Leibler distance,
ICIP12(1681-1684).
IEEE DOI 1302
LC-MS: Liquid chromatography mass spectrometry BibRef

Xu, Y.[Yan], Zhu, J.Y.[Jun-Yan], Chang, E.[Eric], Tu, Z.W.[Zhuo-Wen],
Multiple Clustered Instance Learning for Histopathology Cancer Image Classification, Segmentation and Clustering,
CVPR12(964-971).
IEEE DOI 1208
BibRef

Li, Q.[Quannan], Yao, C.[Cong], Wang, L.[Liwei], Tu, Z.W.[Zhuo-Wen],
Randomness and Sparsity Induced Codebook Learning with Application to Cancer Image Classification,
MCVM12(181-193).
Springer DOI 1305
BibRef
Earlier: MCV12(16-23).
IEEE DOI 1207
BibRef

Yaguchi, A.[Atsushi], Kobayashi, T.[Takumi], Watanabe, K.[Kenji], Iwata, K.[Kenji], Hosaka, T.[Tadaaki], Otsu, N.[Nobuyuki],
Cancer detection from biopsy images using probabilistic and discriminative features,
ICIP11(1609-1612).
IEEE DOI 1201
BibRef

Liu, F.H.[Fang-Hua], Duan, C.[Chaijie], Yuan, K.H.[Ke-Hong], Liang, Z.R.[Zheng-Rong], Bao, S.L.[Shang-Lian],
Detecting Bladder Abnormalities Based on Inter-layer Intensity Curve for Virtual Cystoscopy,
VirtualColon10(76-83).
Springer DOI 1112
BibRef

Wu, Z.[Zhide], Shi, Z.X.[Zheng-Xing], Zhang, G.P.[Guo-Peng], Lu, H.B.[Hong-Bing],
Detection of the Invasion of Bladder Tumor into Adjacent Wall Based on Textural Features Extracted from MRI Images,
VirtualColon10(68-75).
Springer DOI 1112
BibRef

Hans, C.[Charu], Merchant, F.A.[Fatima A.], Shah, S.K.[Shishir K.],
Decision fusion for urine particle classification in multispectral images,
ICCVGIP10(419-426).
DOI Link 1111
BibRef

Kovalev, V.[Vassili], Dmitruk, A.[Alexander], Safonau, I.[Ihar], Frydman, M.[Mikhail], Shelkovich, S.[Sviatlana],
A Method for Identification and Visualization of Histological Image Structures Relevant to the Cancer Patient Conditions,
CAIP11(I: 460-468).
Springer DOI 1109
BibRef

Ståhl, D.[Daniel], Åström, K.[Kalle], Overgaard, N.C.[Niels Christian], Landgren, M.[Matilda], Sjöstrand, K.[Karl], Edenbrandt, L.[Lars],
Automatic Compartment Modelling and Segmentation for Dynamical Renal Scintigraphies,
SCIA11(557-568).
Springer DOI 1105
BibRef

Peskin, A.P.[Adele P.], Dima, A.A.[Alden A.],
Modeling Clinical Tumors to Create Reference Data for Tumor Volume Measurement,
ISVC10(II: 736-746).
Springer DOI 1011
BibRef

Othmani, A.[Ahlem], Meziat, C.[Carole], Loménie, N.[Nicolas],
Ontology-Driven Image Analysis for Histopathological Images,
ISVC10(I: 1-12).
Springer DOI 1011
BibRef

Switonski, A.[Adam], Michalak, M.[Marcin], Josinski, H.[Henryk], Wojciechowski, K.[Konrad],
Detection of Tumor Tissue Based on the Multispectral Imaging,
ICCVG10(II: 325-333).
Springer DOI 1009
BibRef

Michalak, M.[Marcin], Switonski, A.[Adam],
Spectrum Evaluation on Multispectral Images by Machine Learning Techniques,
ICCVG10(II: 126-133).
Springer DOI 1009
BibRef

Schüffler, P.J.[Peter J.], Fuchs, T.J.[Thomas J.], Ong, C.S.[Cheng Soon], Roth, V.[Volker], Buhmann, J.M.[Joachim M.],
Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma,
DAGM10(202-211).
Springer DOI 1009
BibRef

Kiros, R.[Ryan], Popuri, K.[Karteek], Cobzas, D.[Dana], Jagersand, M.[Martin],
Stacked Multiscale Feature Learning for Domain Independent Medical Image Segmentation,
MLMI14(25-32).
Springer DOI 1410
BibRef

Mosayebi, P.[Parisa], Cobzas, D.[Dana], Jagersand, M.[Martin], Murtha, A.[Albert],
Stability effects of finite difference methods on a mathematical tumor growth model,
MMBIA10(125-132).
IEEE DOI 1006
BibRef

De Vylder, J.[Jonas], Rooms, F.[Filip], Philips, W.[Wilfried],
Segmentation of Cell Nuclei in Arabidopsis Thaliana Roots,
ICIAR10(II: 207-216).
Springer DOI 1006
BibRef

Sharif, M.S.[Mhd Saeed], Amira, A.[Abbes],
An intelligent system for pet tumour detection and quantification,
ICIP09(2625-2628).
IEEE DOI 0911
BibRef

Sami, M.M.[Mustafa M.], Saito, M.[Masahisa], Kikuchi, H.[Hisakazu], Saku, T.[Takashi],
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IEEE DOI 0911
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Begelman, G.[Grigory], Rivlin, E.[Ehud],
Automatic screening of bladder cells for cancer diagnosis,
ICIP09(673-676).
IEEE DOI 0911
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Fuchs, T.J.[Thomas J.], Buhmann, J.M.[Joachim M.],
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IEEE DOI 0910
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Fuchs, T.J.[Thomas J.], Haybaeck, J.[Johannes], Wild, P.J.[Peter J.], Heikenwalder, M.[Mathias], Moch, H.[Holger], Aguzzi, A.[Adriano], Buhmann, J.M.[Joachim M.],
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Visual Pattern Analysis in Histopathology Images Using Bag of Features,
CIARP09(521-528).
Springer DOI 0911
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de Vieilleville, F., Lachaud, J.O., Herlin, P., Lezoray, O., Plancoulaine, B.,
Top-Down Segmentation of Histological Images Using a Digital Deformable Model,
ISVC09(I: 327-336).
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Mitrea, D., Nedevschi, S., Lupsor, M., Socaciu, M., Badea, R.,
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CISP09(1-5).
IEEE DOI 0910
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Hattery, D., Hassan, M., Demos, S., Gandjbakhche, A.,
Hyperspectral imaging of Kaposi's Sarcoma for disease assessment and treatment monitoring,
AIPR02(124-130).
IEEE DOI 0210
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Uzgiris, E.E., Lee, D., Sood, A., Bove, K., Lomnes, S.,
Multimodal polymeric contrast agents for MRI and fluorescence imaging in the management of cancer,
AIPR05(133-139).
IEEE DOI 0510
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Aussem, A.[Alex], de Morais, S.R.[Sergio Rodrigues], Corbex, M.[Marilys], Favrel, J.[Joël],
Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods,
GbRPR09(52-61).
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Singh, R.K.[Rahul Kumar], Naik, S.K.[Sarif Kumar], Gupta, L.[Lalit], Balakrishnan, S.[Srinivasan], Santhosh, C., Pai, K.M.[Keerthilatha M.],
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Yao, J.H.[Jian-Hua], Avila, N.[Nilo], Dwyer, A.[Andrew], Taveira-Da Silva, A.M.[Angelo M.], Hathaway, O.M.[Olanda M.], Moss, J.[Joel],
Computer-aided grading of lymphangioleiomyomatosis (LAM) using HRCT,
ICPR08(1-4).
IEEE DOI 0812
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Hontani, H.[Hidekata], Sawada, Y.[Yoshihide],
Stability evaluation of a classifier for detecting abdominal tumors in FDG-PET/CT images,
ICPR08(1-4).
IEEE DOI 0812
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Fuchs, T.J.[Thomas J.], Lange, T.[Tilman], Wild, P.J.[Peter J.], Moch, H.[Holger], Buhmann, J.M.[Joachim M.],
Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Clear Cell Carcinoma,
DAGM08(xx-yy).
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Cobzas, D.[Dana], Birkbeck, N.[Neil], Schmidt, M.[Mark], Jagersand, M.[Martin], Murtha, A.[Albert],
3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set,
MMBIA07(1-8).
IEEE DOI 0710
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Pham, T.D.[Tuan D.],
Mass Spectrometry Based Cancer Classification Using Fuzzy Fractal Dimensions,
CIARP07(614-623).
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Bell, A.A.[Andre A.], Herberich, G.[Gerlind], Meyer-Ebrecht, D.[Dietrich], Bocking, A.[Alfred], Aach, T.[Til],
Segmentation and Detection of Nuclei in Silver Stained Cell Specimens for Early Cancer Diagnosis,
ICIP07(VI: 49-52).
IEEE DOI 0709
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El Naqa, I.[Issam], Yang, D.[Deshan], Deasy, J.O.[Joseph O.],
Automated Estimation of the Biophysical Target for Radiotherapy Treatment Planning using Multimodality Image Analysis,
ICIP07(V: 533-536).
IEEE DOI 0709
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Kaster, F.O.[Frederik O.], Menze, B.H.[Bjoern H.], Weber, M.A.[Marc-André], Hamprecht, F.A.[Fred A.],
Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations,
MCV10(74-85).
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Quelhas, P.[Pedro], Marcuzzo, M.[Monica], Mendonça, A.M.[Ana Maria], Oliveira, M.J.[Maria José], Campilho, A.[Aurelio],
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Görlitz, L., Menze, B.H.[Bjoern H.], Weber, M.A.[Marc-André], Kelm, B.M., Hamprecht, F.A.[Fred A.],
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DAGM07(224-233).
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Yang, S.W.[Si-Wei], Götze, S.[Sandra], Mateos-Langerak, J.[Julio], van Driel, R.[Roel], Eils, R.[Roland], Rohr, K.[Karl],
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DAGM07(497-506).
Springer DOI 0709
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Yu, F.Y.[Fei-Yang], Ip, H.H.S.[Horace H. S.],
Spatial-HMM: A new approach for Semantic Annotation of Histological Images,
ICPR06(IV: 663-666).
IEEE DOI 0609
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Slagmolen, P.[Pieter], Loeckx, D.[Dirk], Roels, S.[Sarah], Geets, X.[Xavier], Maes, F.[Frederik], Haustermans, K.[Karin], Suetens, P.[Paul],
Nonrigid Registration of Multitemporal CT and MR Images for Radiotherapy Treatment Planning,
WBIR06(297-305).
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Yfantis, E.A., Lazarakis, T., Popovich, A., Angelopoulos, A., Bebis, G.N.,
On Cancer Recognition of Ultrasound Images,
CVBVS00(44).
IEEE DOI 0006
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Seo, K.S.[Kyung-Sik],
Automatic Hepatic Tumor Segmentation Using Composite Hypotheses,
ICIAR05(922-929).
Springer DOI 0509
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Flores, A.B.[Aldrin Barreto], Robles, L.A.[Leopoldo Altamirano], Tepalt, R.M.M.[Rosa Maria Morales], Aragon, J.D.C.[Juan D. Cisneros],
Identifying Precursory Cancer Lesions Using Temporal Texture Analysis,
CRV05(34-39).
IEEE DOI 0505
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Miyamoto, T., Iizuka, N., Oka, M., Uchimura, S., Hamamoto, Y.,
Comparison of microarray-based predictive systems for early recurrence of cancer,
ICPR04(II: 347-350).
IEEE DOI 0409
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Tozaki, T., Senda, M., Sakamoto, S., Matsumoto, K.,
Computer assisted diagnosis method of whole body cancer using FDG-PET images,
ICIP03(II: 1085-1088).
IEEE DOI 0312
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Chen, S.R.[Si-Rong], Wong, L.[Longkin], Feng, D.[Dagan],
A new automatic detection approach for hepatocellular, carcinoma using 11C-acetate positron emission tomography,
ICIP03(I: 1065-1068).
IEEE DOI 0312
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Ablameyko, S.V., Kirillov, V., Lagunovsky, D., Patsko, O., Paramonova, N., Petrou, M., Tchij, O.,
From cell image segmentation to differential diagnosis of thyroid cancer,
ICPR02(I: 763-766).
IEEE DOI 0211
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Mohamed, A., Kyriacou, S.K., Davatzikos, C.[Christos],
A Statistical Approach for Estimating Brain Tumor-Induced Deformation,
MMBIA01(xx-yy). 0110
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Spyridonos, P., Ravazoula, P., Cavouras, D., Nikiforidis, G.,
An Automatic Classification System of Urine Bladder Tumors Employing Morphological and Textural Nuclear Features,
ICIP01(II: 853-856).
IEEE DOI 0108
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Lam, R.W.K., Ip, H.H.S., Cheung, K.K.T., Tang, L.H.Y., Hanka, R.,
A Multi-window Approach to Classify Histological Features,
ICPR00(Vol II: 259-262).
IEEE DOI 0009
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Hattery, D., Loew, M., Chernomordik, V., Gandjbakhche, A.,
Optical Signatures of Small, Deeply Embedded, Tumor-like Inclusions in Tissue-like Turbid Media Based on a Random-walk Theory of Photon Migration,
ICPR00(Vol IV: 348-351).
IEEE DOI 0009
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Leung, C.C., Chan, F.H.Y., Lam, K.Y., Kwok, P.C.K., Chen, W.F.,
Thyroid Cancer Cells Boundary Location by a Fuzzy Edge Detection Method,
ICPR00(Vol IV: 360-363).
IEEE DOI 0009
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Brahme, A.,
Towards Inverse Radiation Therapy Planning and Multidimensional Cancer Treatment Optimization,
SSAB97(Medical) 9703
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Choi, H.K.[Heung-Kook], Bengtsson, E.[Ewert], Jarkrans, T.[Torsten], Vasko, J.[Janos], Wester, K.[Kenneth], Malmström, P.U.[Per-Uno], Busch, C.[Christer],
Minimum spanning trees (MST) as a tool for describing tissue architecture when grading bladder carcinoma,
CIAP95(615-620).
Springer DOI 9509
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Thiran, J.P., Macq, B., Mairesse, J.,
Morphological classification of cancerous cells,
ICIP94(III: 706-710).
IEEE DOI 9411
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Fan, N.P.[Ning-Ping], Li, C.C., Fuchs, F.,
Myofibril image processing for studying sarcomere dynamics,
ICPR88(I: 468-472).
IEEE DOI 8811
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Lymph Nodes .


Last update:Nov 18, 2017 at 20:56:18