Automatic mitosis detection in breast histopathology images using Convolutional Neural Network based deep transfer learning

Biocybernetics and Biomedical Engineering - Tập 39 - Trang 214-223 - 2019
K. Sabeena Beevi1, Madhu S. Nair2, G.R. Bindu3
1Electrical & Electronics Department, T. K. M College of Engineering, Kollam, Kerala, India
2Department of Computer Science, Cochin University of Science and Technology, Kochi, Kerala, India
3Electrical Engineering Department, College of Engineering Wayanad, Wayanad, Kerala, India

Tài liệu tham khảo

Malon, 2013, Classification of mitotic figures with convolutional neural networks and seeded blob features, J Pathol Inform, 4, 9, 10.4103/2153-3539.112694

Elston, 1993, Method for grading breast cancer, J Clin Pathol, 46, 189, 10.1136/jcp.46.2.189-b

Barlow, 1977, Changes in chromatin structure during the mitotic cycle, Protoplasma, 91, 207, 10.1007/BF01276735

Dundar, 2011, Computerized classification of intraductal breast lesions using histopathological images, IEEE Trans Biomed Eng, 58, 1977, 10.1109/TBME.2011.2110648

Chen, 2016, Mitosis detection in breast cancer histology images via deep cascaded networks

Cireşan, 2013, Mitosis detection in breast cancer histology images with deep neural networks, 411

Yosinski, 2014, How transferable are features in deep neural networks?, 3320

Xu, 2014, Deep learning of feature representation with multiple instance learning for medical image analysis, 1626

Gandomi, 2012, Krill herd: a new bio-inspired optimization algorithm, Commun Nonlinear Sci Numer Simul, 17, 4831, 10.1016/j.cnsns.2012.05.010

K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556.

Jia, 2013

MITOS, ICPR 2014 Contest, 2014

Angshuman, 2015, Mitosis detection for invasive breast cancer grading in histopathological images, IEEE Trans Image Process, 24, 4041, 10.1109/TIP.2015.2460455

Beevi, 2017, A multi-classifier system for automatic mitosis detection in breast histopathology images using deep belief networks, IEEE J Transl Eng Health Med, 5, 1, 10.1109/JTEHM.2017.2694004

Belien, 1997, Counting mitoses by image processing in Feulgen stained breast cancer sections: the influence of resolution, Cytometry Part A, 28, 135, 10.1002/(SICI)1097-0320(19970601)28:2<135::AID-CYTO6>3.0.CO;2-E

Sertel, 2009, Computeraided prognosis of neuroblastoma: detection of mitosis and karyorrhexis cells in digitized histological images, 1433

Irshad, 2013, Multi-channels statistical and morphological features based mitosis detection in breast cancer histopathology, 6091

Khan, 2012, A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images, 149

Roullier, 2011, Multi-resolution graph-based analysis of histopathological whole slide images: application to mitotic cell extraction and visualization, Comput Med Imaging Graphics, 35, 603, 10.1016/j.compmedimag.2011.02.005

Anari, 2010, Computer-aided detection of proliferative cells and mitosis index in immunohistichemically images of meningioma, 1

Beevi, 2014, Automatic segmentation and classification of mitotic cell nuclei in histopathology images based on active contour model, 740

Irshad, 2013, Automated mitosis detection using texture, sift features and hmax biologically inspired approach, J Pathol Inform, 4, 12, 10.4103/2153-3539.109870

Tek, 2013, Mitosis detection using generic features and an ensemble of cascade adaboosts, J Pathol Inform, 4, 12, 10.4103/2153-3539.112697

Beevi, 2016, Detection of mitotic nuclei in breast histopathology images using localized acm and random kitchen sink based classifier, 2435

Doyle, 2008, Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features, 496

Lu, 2014, Toward automatic mitotic cell detection and segmentation in multispectral histopathological images, IEEE J Biomed Health Inform, 18, 594, 10.1109/JBHI.2013.2277837

Wang, 2014, Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection, 90410B

Gopakumar, 2017, Cytopathological image analysis using deep-learning networks in microfluidic microscopy, JOSA A, 34, 111, 10.1364/JOSAA.34.000111

Albayrak, 2016, Mitosis detection using convolutional neural network based features, 000335

Li, 2018, Efficient and accurate mitosis detection-a lightweight rcnn approach, 69

Malon, 2013, Classification of mitotic figures with convolutional neural networks and seeded blob features, J Pathol Inform, 4, 9, 10.4103/2153-3539.112694

Sirinukunwattana, 2016, Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images, IEEE Trans Med Imaging, 35, 1196, 10.1109/TMI.2016.2525803

Wan, 2017, Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features, Neurocomputing, 229, 34, 10.1016/j.neucom.2016.05.084

Ciresan, 2011, Flexible, high performance convolutional neural networks for image classification, vol. 22, 1237

Khan, 2014, A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution, IEEE Trans Biomed Eng, 61, 1729, 10.1109/TBME.2014.2303294

Beevi, 2016, Automatic segmentation of cell nuclei using krill herd optimization based multi-thresholding and localized active contour model, Biocybern Biomed Eng, 36, 584, 10.1016/j.bbe.2016.06.005

Vedaldi, 2014

Sharif Razavian, 2014, Azizpour, Cnn features off-the-shelf: an astounding baseline for recognition, 806

Jolliffe, 2002, Principal component analysis for special types of data, 338

Blum, 1997, Selection of relevant features and examples in machine learning, Artif Intell, 97, 245, 10.1016/S0004-3702(97)00063-5

Thông tin
Thông tin xuất bản

Biocybernetics and Biomedical Engineering

Tập 39

214-223

Thông tin tác giả