A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images

References

• Ali S, Madabhushi A. 2012. An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery. IEEE Trans Med Imaging. 31:1448–1460.
   PubMed Web of Science ®Google Scholar
• Basavanhally A, Feldman M, Shih N, Mies C, Tomaszewski J, Ganesan S, Madabhushi A. 2011. Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to oncotype dx. J Pathol Inform. 2:S1. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22811953
   PubMedGoogle Scholar
• Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. 2013. Mitosis detection in breast cancer histology images with deep neural networks. In: MICCAI. vol. 2, p. 411–418.
   Google Scholar
• Cruz-Roa A, Basavanhally A, González F, Gilmore H, Feldman M, Ganesan S, Shih N, Tomaszewski J, Madabhushi A. 2014. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In: SPIE Medical Imaging. vol. 9041, p. 904103–904115.
   Google Scholar
• Doyle S, Feldman M, Tomaszewski J, Madabhushi A. 2012. A boosted bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies. Trans Biomed Eng. 59:1205–1218.
   PubMed Web of Science ®Google Scholar
• Duchi J, Hazan E, Singer Y. 2011. Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res. 12:2121–2159.
   Web of Science ®Google Scholar
• Fatakdawala H, Basavanhally A, Jun Xu, Bhanot G, Ganesan S, Feldman M, Tomaszewski J, Madabhushi A. 2010. Expectation-maximization-driven geodesic active contour with overlap resolution (emagacor): Application to lymphocyte segmentation on breast cancer histopathology. IEEE Trans Biomed Eng. 57:1676–1689.
   PubMed Web of Science ®Google Scholar
• Genestie C, Zafrani B, Asselain B, Fourquet A, Rozan S, Validire P, Vincent-Salomon A, Sastre-Garau X. 1998. Comparison of the prognostic value of scarff-bloom-richardson and nottingham histological grades in a series of 825 cases of breast cancer: Major importance of the mitotic count as a component of both grading systems. Anticancer Res. 18:571–576.
   PubMed Web of Science ®Google Scholar
• Humphrey PA. 2004. Gleason grading and prognostic factors in carcinoma of the prostate. Mod Pathol. 17:292–306.
   PubMed Web of Science ®Google Scholar
• Irshad H, Veillard A, Roux L, Racoceanu D. 2014. Methods for nuclei detection, segmentation, and classification in digital histopathology: A review-current status and future potential. IEEE Rev Biomed Eng. 7:97–114.
   PubMedGoogle Scholar
• Janowczyk A, Chandran S, Singh R, Sasaroli D, Coukos G, Feldman MD, Madabhushi A. 2012. High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts. Trans Biomed Eng. 59:1240–1252.
   PubMed Web of Science ®Google Scholar
• Jia Y, Shelhamer E, Donahue J, Karayev S. 2014. Caffe: convolutional architecture for fast feature embedding. arXiv:1408.5093
   Google Scholar
• Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. Adv Neur Info Proc Sys. 1097–1105.
   Google Scholar
• Ruifrok AC, Johnston DA. 2001. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol. 23:291–299.
   PubMed Web of Science ®Google Scholar
• Veta M, van Diest PJ, Kornegoor R, Huisman A, Viergever MA, Pluim JPW. 2013. Automatic nuclei segmentation in H &E stained breast cancer histopathology images. PLoS One. 8:e70221.
   PubMed Web of Science ®Google Scholar
• Wang H, Cruz-Roa A, Basavanhally A, Gilmore H, Shih N, Feldman M, Tomaszewski J, Gonzalez F, Madabhush A. 2014. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J Med Imaging. 1:034003.
   Google Scholar
• Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A. 2015. Stacked sparse autoencoder (ssae) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging.
   Web of Science ®Google Scholar