21.7.2.1 Breast Cancer Cell Analysis, Pathology, Nuclei Detection

Raimondo, F., Gavrielides, M.A., Karayannopoulou, G., Lyroudia, K., Pitas, I., Kostopoulos, I.,
Automated Evaluation of Her-2/neu Status in Breast Tissue From Fluorescent In Situ Hybridization Images,
IP(14), No. 9, September 2005, pp. 1288-1299.
IEEE DOI 0508
BibRef

Harrabi, R., Ben Braiek, E.,
Color image segmentation using multi-level thresholding approach and data fusion techniques: application in the breast cancer cells images,
JIVP(2012), No. 1 2012, pp. xx-yy.
DOI Link 1205
BibRef

Zhang, X.F.[Xiao-Fan], Liu, W.[Wei], Dundar, M.[Murat], Badve, S.I.[Sun-Il], Zhang, S.T.[Shao-Ting],
Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval,
MedImg(34), No. 2, February 2015, pp. 496-506.
IEEE DOI 1502
Breast cancer BibRef

Zhang, X.F.[Xiao-Fan], Su, H.[Hai], Yang, L.[Lin], Zhang, S.T.[Shao-Ting],
Fine-grained histopathological image analysis via robust segmentation and large-scale retrieval,
CVPR15(5361-5368)
IEEE DOI 1510
BibRef

Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., Madabhushi, A.,
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images,
MedImg(35), No. 1, January 2016, pp. 119-130.
IEEE DOI 1601
Breast cancer BibRef

Ehteshami Bejnordi, B., Balkenhol, M., Litjens, G., Holland, R., Bult, P., Karssemeijer, N., van der Laak, J.A.W.M.,
Automated Detection of DCIS in Whole-Slide H E Stained Breast Histopathology Images,
MedImg(35), No. 9, September 2016, pp. 2141-2150.
IEEE DOI 1609
Breast tissue BibRef

Wdowiak, M.[Marek], Markiewicz, T.[Tomasz], Osowski, S.[Stanislaw], Patera, J.[Janusz], Kozlowski, W.[Wojciech],
Novel segmentation algorithm for identification of cell membrane staining in HER2 images,
PRL(84), No. 1, 2016, pp. 225-231.
Elsevier DOI 1612
Pattern recognition BibRef

Xing, F., Xie, Y., Yang, L.,
An Automatic Learning-Based Framework for Robust Nucleus Segmentation,
MedImg(35), No. 2, February 2016, pp. 550-566.
IEEE DOI 1602
Breast cancer BibRef

Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., Navab, N.,
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images,
MedImg(35), No. 5, May 2016, pp. 1313-1321.
IEEE DOI 1605
Biomedical imaging BibRef

Mercan, C., Aksoy, S.[Selim], Mercan, E.[Ezgi], Shapiro, L.G.[Linda G.], Weaver, D.L.[Donald L.], Elmore, J.G.[Joann G.],
Multi-Instance Multi-Label Learning for Multi-Class Classification of Whole Slide Breast Histopathology Images,
MedImg(37), No. 1, January 2018, pp. 316-325.
IEEE DOI 1801
biomedical optical imaging, cancer, image classification, learning (artificial intelligence), medical image processing, whole slide imaging BibRef

Mercan, E.[Ezgi], Aksoy, S.[Selim], Shapiro, L.G.[Linda G.], Weaver, D.L.[Donald L.], Brunye, T.[Tad], Elmore, J.G.[Joann G.],
Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images,
ICPR14(1179-1184)
IEEE DOI 1412
Accuracy BibRef

Saha, M., Chakraborty, C.,
Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation,
IP(27), No. 5, May 2018, pp. 2189-2200.
IEEE DOI 1804
cancer, feature extraction, image classification, image segmentation, learning (artificial intelligence), nuclei BibRef

Gecer, B.[Baris], Aksoy, S.[Selim], Mercan, E.[Ezgi], Shapiro, L.G.[Linda G.], Weaver, D.L.[Donald L.], Elmore, J.G.[Joann G.],
Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks,
PR(84), 2018, pp. 345-356.
Elsevier DOI 1809
Digital pathology, Breast histopathology, Whole slide imaging, Region of interest detection, Saliency detection, Deep learning BibRef

Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., Litjens, G., van der Laak, J., Ciompi, F.,
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks,
MedImg(37), No. 9, September 2018, pp. 2126-2136.
IEEE DOI 1809
Breast cancer, Standards, Tumors, Pathology, Training, Image analysis, Image color analysis, Breast cancer, mitosis detection, knowledge distillation BibRef

Tellez, D.[David], Litjens, G.[Geert], van der Laak, J.[Jeroen], Ciompi, F.[Francesco],
Neural Image Compression for Gigapixel Histopathology Image Analysis,
PAMI(43), No. 2, February 2021, pp. 567-578.
IEEE DOI 2101
Image coding, Training, Image reconstruction, Image analysis, Neural networks, Visualization, Task analysis, representation learning BibRef

Yang, H., Kim, J., Kim, H., Adhikari, S.P.,
Guided Soft Attention Network for Classification of Breast Cancer Histopathology Images,
MedImg(39), No. 5, May 2020, pp. 1306-1315.
IEEE DOI 2005
Breast cancer, Microscopy, Noise measurement, Neural networks, Pathology, Training, Task analysis, Breast cancer, microscopy image, pattern recognition and classification BibRef

Saxena, S.[Shweta], Shukla, S.[Sanyam], Gyanchandani, M.[Manasi],
Breast cancer histopathology image classification using kernelized weighted extreme learning machine,
IJIST(31), No. 1, 2021, pp. 168-179.
DOI Link 2102
breast cancer, computer-aided diagnosis, histopathology, pretrained convolutional neural network BibRef

Alkassar, S., Jebur, B.A.[Bilal A.], Abdullah, M.A.M.[Mohammed A. M.], Al-Khalidy, J.H.[Joanna H.], Chambers, J.A.,
Going deeper: magnification-invariant approach for breast cancer classification using histopathological images,
IET-CV(15), No. 2, 2021, pp. 151-164.
DOI Link 2106
BibRef

Hu, C.H.[Chu-Han], Sun, X.Y.[Xiao-Yan], Yuan, Z.M.[Zhen-Ming], Wu, Y.F.[Ying-Fei],
Classification of breast cancer histopathological image with deep residual learning,
IJIST(31), No. 3, 2021, pp. 1583-1594.
DOI Link 2108
data augmentation, histopathological image, myResNet-34, stain normalization BibRef

Gupta, A.P.[Amar Prasad], Yeo, S.J.[Seung Jun], Mativenga, M.[Mallory], Jung, J.[Jaeik], Kim, W.[Wooseob], Lim, J.[Jongmin], Park, J.Y.[Jun-Young], Ahn, J.S.[Jeung Sun], Kim, S.H.[Seung Hoon], Chae, M.S.[Moon Shik], Yeon, Y.H.[Yeong Heum], Kim, N.[Namkug], Ko, B.S.[Beom-Seok], Ryu, J.[Jehwang],
A feasibility study of a portable intraoperative specimen imaging X-ray system based on carbon nanotube field emitters,
IJIST(31), No. 3, 2021, pp. 1128-1135.
DOI Link 2108
breast cancer, carbon nanotube emitter, field emission, pathological study, surgical margin, tumor, X-ray imaging BibRef

Zou, Y.[Ying], Zhang, J.X.[Jian-Xin], Huang, S.[Shan], Liu, B.[Bin],
Breast cancer histopathological image classification using attention high-order deep network,
IJIST(32), No. 1, 2022, pp. 266-279.
DOI Link 2201
breast cancer histopathological image classification, convolutional neural network, covariance pooling, second-order statistics BibRef

Huang, H.[Hui], Feng, X.[Xi'an], Jiang, J.[Jionghui], Chen, P.Y.[Pei-Yu], Zhou, S.[Suying],
Mask RCNN algorithm for nuclei detection on breast cancer histopathological images,
IJIST(32), No. 1, 2022, pp. 209-217.
DOI Link 2201
breast cancer histopathological, Mask R-CNN algorithm, nuclei detection BibRef

Melekoodappattu, J.G.[Jayesh George], Dhas, A.S.[Anto Sahaya], Kumar, K.B.[K. Binil], Adarsh, K.S.,
Malignancy detection on mammograms by integrating modified convolutional neural network classifier and texture features,
IJIST(32), No. 2, 2022, pp. 564-574.
DOI Link 2203
accuracy, CNN, ensemble method, MVU, texture feature BibRef

Thiagarajan, P.[Ponkrshnan], Khairnar, P.[Pushkar], Ghosh, S.[Susanta],
Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images,
MedImg(41), No. 4, April 2022, pp. 815-825.
IEEE DOI 2204
Uncertainty, Bayes methods, Convolutional neural networks, Breast cancer, Neural networks, Cancer, Transfer learning, t-SNE BibRef

Abdelli, A.[Adel], Saouli, R.[Rachida], Djemal, K.[Khalifa], Youkana, I.[Imane],
Multiple instance learning for classifying histopathological images of the breast cancer using residual neural network,
IJIST(32), No. 3, 2022, pp. 1015-1029.
DOI Link 2205
breast cancer, convolutional neural networks, histopathological images, multiple instances learning BibRef

Mathew, T.[Tojo], Ajith, B., Kini, J.R.[Jyoti R.], Rajan, J.[Jeny],
Deep learning-based automated mitosis detection in histopathology images for breast cancer grading,
IJIST(32), No. 4, 2022, pp. 1192-1208.
DOI Link 2207
breast cancer, cancer grading, deep learning, histopathology, mitosis detection BibRef

Liu, K.[Kun], Liu, Z.L.[Zhuo-Lin], Liu, S.[Sidong],
Semi-Supervised Breast Histopathological Image Classification with Self-Training Based on Non-Linear Distance Metric,
IET-IPR(16), No. 12, 2022, pp. 3164-3176.
DOI Link 2209
BibRef

Lu, Y.Y.[Yuan-Yue], Zhang, J.[Jun], Liu, X.[Xueyu], Zhang, Z.H.[Zhi-Hong], Li, W.X.[Wang-Xing], Zhou, X.S.[Xiao-Shuang], Li, R.S.[Rong-Shan],
Prediction of breast cancer metastasis by deep learning pathology,
IET-IPR(17), No. 2, 2023, pp. 533-543.
DOI Link 2302
BibRef

Shi, J.B.[Jiang-Bo], Tang, L.[Lufei], Li, Y.[Yang], Zhang, X.L.[Xian-Li], Gao, Z.[Zeyu], Zheng, Y.F.[Ye-Feng], Wang, C.B.[Chun-Bao], Gong, T.[Tieliang], Li, C.[Chen],
A Structure-Aware Hierarchical Graph-Based Multiple Instance Learning Framework for pT Staging in Histopathological Image,
MedImg(42), No. 10, October 2023, pp. 3000-3011.
IEEE DOI 2310
BibRef

Zhong, L.[Lanfeng], Wang, G.[Guotai], Liao, X.[Xin], Zhang, S.T.[Shao-Ting],
HAMIL: High-Resolution Activation Maps and Interleaved Learning for Weakly Supervised Segmentation of Histopathological Images,
MedImg(42), No. 10, October 2023, pp. 2912-2923.
IEEE DOI 2310
BibRef

Lou, W.[Wei], Wan, X.[Xiang], Li, G.B.[Guan-Bin], Lou, X.Y.[Xiao-Ying], Li, C.[Chenghang], Gao, F.[Feng], Li, H.F.[Hao-Feng],
Structure Embedded Nucleus Classification for Histopathology Images,
MedImg(43), No. 9, September 2024, pp. 3149-3160.
IEEE DOI Code:
WWW Link. 2409
Feature extraction, Shape, Graph neural networks, Histopathology, Task analysis, Decoding, Image edge detection, graph neural network BibRef

Fernandes, S.L.[Steven L.], Krivic, S.[Senka], Sharma, P.[Poonam], Jha, S.K.[Sumit K.],
Attribution-based Confidence Metric for Detection of Adversarial Attacks on Breast Histopathological Images,
AdvRob22(501-516).
Springer DOI 2304
BibRef

Chhipa, P.C.[Prakash Chandra], Upadhyay, R.[Richa], Pihlgren, G.G.[Gustav Grund], Saini, R.[Rajkumar], Uchida, S.[Seiichi], Liwicki, M.[Marcus],
Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images,
WACV23(2716-2726)
IEEE DOI 2302
Representation learning, Histopathology, Microscopy, Supervised learning, Redundancy, Focusing, medical images BibRef

Liu, S.J.[Sheng-Jie], Zhu, C.[Chuang], Xu, F.[Feng], Jia, X.Y.[Xin-Yu], Shi, Z.Y.[Zhong-Yue], Jin, M.[Mulan],
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix,
CVMI22(1814-1823)
IEEE DOI 2210
Pathology, Image synthesis, Breast tissue, Epidermis, Breast cancer BibRef

Iheme, L.O.[Leonardo O.], Solmaz, G.[Gizem], Tokat, F.[Fatma], Çayir, S.[Sercan], Bozaba, E.[Engin], Yazici, Ç.[Çisem], Özsoy, G.[Gülsah], Ayalti, S.[Samet], Kayhan, C.K.[Cavit Kerem], Ince, Ü.[Ümit],
Patch-Level Nuclear Pleomorphism Scoring Using Convolutional Neural Networks,
CAIP21(I:185-194).
Springer DOI 2112
Examining Hematoxylin and Eosin stained breast tissue. BibRef

Li, Z.Q.[Zi-Qiang], Tao, R.[Rentuo], Wu, Q.[Qianrun], Li, B.[Bin],
DA-RefineNet: Dual-inputs Attention RefineNet for Whole Slide Image Segmentation,
ICPR21(1918-1925)
IEEE DOI 2105
Knowledge engineering, Image segmentation, Semantics, Breast, Feature extraction, Optical imaging, Pattern recognition BibRef

Li, B.[Beibin], Mercan, E.[Ezgi], Mehta, S.[Sachin], Knezevich, S.[Stevan], Arnold, C.W.[Corey W.], Weaver, D.L.[Donald L.], Elmore, J.G.[Joann G.], Shapiro, L.G.[Linda G.],
Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline,
ICPR21(8727-8734)
IEEE DOI 2105
Image segmentation, Annotations, Histopathology, Pipelines, Semantics, Training data, Breast, biomedical imaging, deep learning, whole slide images BibRef

Ma, X.[Xuru],
A Classification Method of Breast Pathological Image Based on Residual Learning,
CVIDL20(135-139)
IEEE DOI 2102
cancer, convolutional neural nets, diseases, feature extraction, image classification, image segmentation, image texture, data augmentation BibRef

Roszkowiak, L.[Lukasz], Zak, J.[Jakub], Siemion, K.[Krzysztof], Pijanowska, D.[Dorota], Korzynska, A.[Anna],
Nuclei Detection with Local Threshold Processing in Dab&h Stained Breast Cancer Biopsy Images,
ICCVG20(164-175).
Springer DOI 2009
BibRef

Ma, M., Shi, Y., Li, W., Gao, Y., Xu, J.,
A Novel Two-Stage Deep Method for Mitosis Detection in Breast Cancer Histology Images,
ICPR18(3892-3897)
IEEE DOI 1812
Feature extraction, Task analysis, Convolution, Training, Breast cancer, Shape BibRef

Rakhlin, A.[Alexander], Shvets, A.[Alexey], Iglovikov, V.[Vladimir], Kalinin, A.A.[Alexandr A.],
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis,
ICIAR18(737-744).
Springer DOI 1807
BibRef

Wang, Z.[Zeya], Dong, N.Q.[Nan-Qing], Dai, W.[Wei], Rosario, S.D.[Sean D.], Xing, E.P.[Eric P.],
Classification of Breast Cancer Histopathological Images using Convolutional Neural Networks with Hierarchical Loss and Global Pooling,
ICIAR18(745-753).
Springer DOI 1807
BibRef

Mahbod, A.[Amirreza], Ellinger, I.[Isabella], Ecker, R.[Rupert], Smedby, Ö.[Örjan], Wang, C.L.[Chun-Liang],
Breast Cancer Histological Image Classification Using Fine-Tuned Deep Network Fusion,
ICIAR18(754-762).
Springer DOI 1807
BibRef

Ferreira, C.A.[Carlos A.], Melo, T.[Tânia], Sousa, P.[Patrick], Meyer, M.I.[Maria Inês], Shakibapour, E.[Elham], Costa, P.[Pedro], Campilho, A.[Aurélio],
Classification of Breast Cancer Histology Images Through Transfer Learning Using a Pre-trained Inception Resnet V2,
ICIAR18(763-770).
Springer DOI 1807
BibRef

Brancati, N.[Nadia], Frucci, M.[Maria], Riccio, D.[Daniel],
Multi-classification of Breast Cancer Histology Images by Using a Fine-Tuning Strategy,
ICIAR18(771-778).
Springer DOI 1807
BibRef

Cao, H.[Hongliu], Bernard, S.[Simon], Heutte, L.[Laurent], Sabourin, R.[Robert],
Improve the Performance of Transfer Learning Without Fine-Tuning Using Dissimilarity-Based Multi-view Learning for Breast Cancer Histology Images,
ICIAR18(779-787).
Springer DOI 1807
BibRef

Awan, R.[Ruqayya], Koohbanani, N.A.[Navid Alemi], Shaban, M.[Muhammad], Lisowska, A.[Anna], Rajpoot, N.[Nasir],
Context-Aware Learning Using Transferable Features for Classification of Breast Cancer Histology Images,
ICIAR18(788-795).
Springer DOI 1807
BibRef

Koné, I.[Ismaël], Boulmane, L.[Lahsen],
Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification,
ICIAR18(796-803).
Springer DOI 1807
BibRef

Chennamsetty, S.S.[Sai Saketh], Safwan, M.[Mohammed], Alex, V.[Varghese],
Classification of Breast Cancer Histology Image using Ensemble of Pre-trained Neural Networks,
ICIAR18(804-811).
Springer DOI 1807
BibRef

Vesal, S.[Sulaiman], Ravikumar, N.[Nishant], Davari, A.[AmirAbbas], Ellmann, S.[Stephan], Maier, A.[Andreas],
Classification of Breast Cancer Histology Images Using Transfer Learning,
ICIAR18(812-819).
Springer DOI 1807
BibRef

Galal, S.[Sameh], Sanchez-Freire, V.[Veronica],
Candy Cane: Breast Cancer Pixel-Wise Labeling with Fully Convolutional Densenets,
ICIAR18(820-826).
Springer DOI 1807
BibRef

Guo, Y.[Yao], Dong, H.[Huihui], Song, F.[Fangzhou], Zhu, C.[Chuang], Liu, J.[Jun],
Breast Cancer Histology Image Classification Based on Deep Neural Networks,
ICIAR18(827-836).
Springer DOI 1807
BibRef

Golatkar, A.[Aditya], Anand, D.[Deepak], Sethi, A.[Amit],
Classification of Breast Cancer Histology Using Deep Learning,
ICIAR18(837-844).
Springer DOI 1807
BibRef

Wang, Y.Q.[Ya-Qi], Sun, L.L.[Ling-Ling], Ma, K.Q.[Kai-Qiang], Fang, J.N.[Jian-Nan],
Breast Cancer Microscope Image Classification Based on CNN with Image Deformation,
ICIAR18(845-852).
Springer DOI 1807
BibRef

Iesmantas, T.[Tomas], Alzbutas, R.[Robertas],
Convolutional Capsule Network for Classification of Breast Cancer Histology Images,
ICIAR18(853-860).
Springer DOI 1807
BibRef

Marami, B.[Bahram], Prastawa, M.[Marcel], Chan, M.[Monica], Donovan, M.[Michael], Fernandez, G.[Gerardo], Zeineh, J.[Jack],
Ensemble Network for Region Identification in Breast Histopathology Slides,
ICIAR18(861-868).
Springer DOI 1807
BibRef

Nawaz, W.[Wajahat], Ahmed, S.[Sagheer], Tahir, A.[Ali], Khan, H.A.[Hassan Aqeel],
Classification Of Breast Cancer Histology Images Using ALEXNET,
ICIAR18(869-876).
Springer DOI 1807
BibRef

Pimkin, A.[Artem], Makarchuk, G.[Gleb], Kondratenko, V.[Vladimir], Pisov, M.[Maxim], Krivov, E.[Egor], Belyaev, M.[Mikhail],
Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation,
ICIAR18(877-886).
Springer DOI 1807
BibRef

Sarmiento, A.[Auxiliadora], Fondón, I.[Irene],
Automatic Breast Cancer Grading of Histological Images Based on Colour and Texture Descriptors,
ICIAR18(887-894).
Springer DOI 1807
BibRef

Vu, Q.D.[Quoc Dang], To, M.N.N.[Minh Nguyen Nhat], Kim, E.[Eal], Kwak, J.T.[Jin Tae],
Micro and Macro Breast Histology Image Analysis by Partial Network Re-use,
ICIAR18(895-902).
Springer DOI 1807
BibRef

Kohl, M.[Matthias], Walz, C.[Christoph], Ludwig, F.[Florian], Braunewell, S.[Stefan], Baust, M.[Maximilian],
Assessment of Breast Cancer Histology Using Densely Connected Convolutional Networks,
ICIAR18(903-913).
Springer DOI 1807
BibRef

Vang, Y.S.[Yeeleng S.], Chen, Z.[Zhen], Xie, X.H.[Xiao-Hui],
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification,
ICIAR18(914-922).
Springer DOI 1807
BibRef

Huang, C.H.[Chao-Hui], Brodbeck, J.[Jens], Dimaano, N.M.[Nena M.], Kang, J.[John], Dogdas, B.[Belma], Rollins, D.[Douglas], Gifford, E.M.[Eric M.],
Automated Breast Cancer Image Classification Based on Integration of Noisy-And Model and Fully Connected Network,
ICIAR18(923-930).
Springer DOI 1807
BibRef

Nazeri, K.[Kamyar], Aminpour, A.[Azad], Ebrahimi, M.[Mehran],
Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification,
ICIAR18(717-726).
Springer DOI 1807
BibRef

Kwok, S.[Scotty],
Multiclass Classification of Breast Cancer in Whole-Slide Images,
ICIAR18(931-940).
Springer DOI 1807
BibRef

Garud, H., Karri, S.P.K., Sheet, D., Chatterjee, J., Mahadevappa, M., Ray, A.K., Ghosh, A., Maity, A.K.,
High-Magnification Multi-views Based Classification of Breast Fine Needle Aspiration Cytology Cell Samples Using Fusion of Decisions from Deep Convolutional Networks,
Microscopy17(828-833)
IEEE DOI 1709
Breast cancer, Computer architecture, Microprocessors, Microscopy, Training BibRef

Bhandari, S.H.[Smriti H.],
A bag-of-features approach for malignancy detection in breast histopathology images,
ICIP15(4932-4936)
IEEE DOI 1512
bag-of-features; breast histopathology; cancer detection BibRef

Moncayo, R.[Ricardo], Romo-Bucheli, D.[David], Romero, E.[Eduardo],
A Grading Strategy for Nuclear Pleomorphism in Histopathological Breast Cancer Images Using a Bag of Features (BOF),
CIARP15(75-82).
Springer DOI 1511
BibRef

Wdowiak, M.[Marek], Markiewicz, T.[Tomasz], Osowski, S.[Stanislaw], Patera, J.[Janusz], Kozlowski, W.[Wojciech],
Gradients and Active Contour Models for Localization of Cell Membrane in HER2/neu Images,
CAIP15(I:432-444).
Springer DOI 1511
BibRef

Wdowiak, M.[Marek], Markiewicz, T.[Tomasz], Osowski, S.[Stanislaw], Swiderska, Z.[Zaneta], Patera, J.[Janusz], Kozlowski, W.[Wojciech],
Hourglass Shapes in Rank Grey-Level Hit-or-miss Transform for Membrane Segmentation in HER2/neu Images,
ISMM15(3-14).
Springer DOI 1506
BibRef

Khan, A.M.[Adnan Mujahid], Sirinukunwattana, K.[Korsuk], Rajpoot, N.[Nasir],
Geodesic Geometric Mean of Regional Covariance Descriptors as an Image-Level Descriptor for Nuclear Atypia Grading in Breast Histology Images,
MLMI14(101-108).
Springer DOI 1410
BibRef

Pollanen, I., Braithwaite, B., Ikonen, T., Niska, H., Haataja, K., Toivanen, P., Tolonen, T.,
Computer-aided breast cancer histopathological diagnosis: Comparative analysis of three DTOCS-based features: SW-DTOCS, SW-WDTOCS and SW-3-4-DTOCS,
IPTA14(1-6)
IEEE DOI 1503
cancer BibRef

Zheng, Y.S.[Yu-Shan], Jiang, Z.G.[Zhi-Guo], Shi, J.[Jun], Ma, Y.B.[Yi-Bing],
Retrieval of pathology image for breast cancer using PLSA model based on texture and pathological features,
ICIP14(2304-2308)
IEEE DOI 1502
Breast cancer BibRef

Paramanandam, M.[Maqlin], Thamburaj, R.[Robinson], Manipadam, M.T.[Marie Theresa], Nagar, A.K.[Atulya K.],
Boundary Extraction for Imperfectly Segmented Nuclei in Breast Histopathology Images: A Convex Edge Grouping Approach,
IWCIA14(250-261).
Springer DOI 1405
BibRef

Saha, B.N.[Baidya Nath], Saini, A.[Amritpal], Ray, N.[Nilanjan], Greiner, R.[Russell], Hugh, J.[Judith], Tambasco, M.[Mauro],
A robust convergence index filter for breast cancer cell segmentation,
ICIP14(922-926)
IEEE DOI 1502
Convergence BibRef

Wan, T.[Tao], Liu, X.[Xu], Chen, J.H.[Jian-Hui], Qin, Z.C.[Zeng-Chang],
Wavelet-based statistical features for distinguishing mitotic and non-mitotic cells in breast cancer histopathology,
ICIP14(2290-2294)
IEEE DOI 1502
Breast cancer BibRef

Tripathi, A.S.[Ardhendu Shekhar], Mathur, A.[Atin], Daga, M.[Mohit], Kuse, M.[Manohar], Au, O.C.[Oscar C.],
2-SiMDoM: A 2-Sieve model for detection of mitosis in multispectral breast cancer imagery,
ICIP13(611-615)
IEEE DOI 1402
Accuracy BibRef

Khan, A.M.[Adnan M.], El-Daly, H.[Hesham], Rajpoot, N.M.[Nasir M.],
A Gamma-Gaussian mixture model for detection of mitotic cells in breast cancer histopathology images,
ICPR12(149-152).
WWW Link. 1302
BibRef

Peskin, A.P.[Adele P.], Hoeppner, D.J.[Daniel J.], Stuelten, C.H.[Christina H.],
Segmentation and Cell Tracking of Breast Cancer Cells,
ISVC11(I: 381-391).
Springer DOI 1109
BibRef

Roullier, V.[Vincent], Lézoray, O.[Olivier], Ta, V.T.[Vinh-Thong], El Moataz, A.[Abderrahim],
Mitosis Extraction in Breast-Cancer Histopathological Whole Slide Images,
ISVC10(I: 539-548).
Springer DOI 1011
Not mammogram, but analysis of tissue samples. BibRef

Tsapatsoulis, N., Schnorrenberg, F., Pattichis, C.S., and Kollias, S.,
An Image Analysis System for Automated Detection of Breast Cancer Nuclei,
ICIP97(III: 512-515).
IEEE DOI BibRef 9700

Dias, A.V., Bortolozzi, F., Delgado, M.R.B.S.,
Results of the Use of Bayesian Classifiers for Identification of Breast Cancer Cell Nuclei,
ICPR96(III: 508-512).
IEEE DOI 9608
(Centro Federal de Educacao, BR) BibRef

Murshed, N.A., Bortolozzi, F., Sabourin, R.,
A fuzzy ARTMAP-based classification system for detecting cancerous cells, based on the one-class problem approach,
ICPR96(IV: 478-482).
IEEE DOI 9608
(Centro Federal de Educacao, BR) BibRef

Mea, V.D.[Vincenzo Della], Beltrami, C.A.[Carlo Alberto],
Analysis of the spatial arrangement of cells in the proliferative breast lesions,
CIAP95(247-252).
Springer DOI 9509
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Mammography, Microcalcifications, Detection, Analysis .