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Localization and classification of cell nuclei in post-neoadjuvant breast cancer surgical specimen using fully convolutional networks.

, , , , , , and . Medical Imaging: Digital Pathology, volume 10581 of SPIE Proceedings, page 105810O. SPIE, (2018)

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Kernelized Supervised Dictionary Learning.. University of Waterloo, Ontario, Canada, (2013)base-search.net (ftunivwaterloo:oai:uwspace.uwaterloo.ca:10012/7455).Localization and classification of cell nuclei in post-neoadjuvant breast cancer surgical specimen using fully convolutional networks., , , , , , and . Medical Imaging: Digital Pathology, volume 10581 of SPIE Proceedings, page 105810O. SPIE, (2018)Response monitoring using quantitative ultrasound methods and supervised dictionary learning in locally advanced breast cancer., , , , and . Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 978413. SPIE, (2016)End-to-End Unsupervised Document Image Blind Denoising., , , and . CoRR, (2021)Unsupervised Visual Changepoint Detection Using Maximum Mean Discrepancy., , and . ICIAR, volume 7950 of Lecture Notes in Computer Science, page 336-345. Springer, (2013)Dictionary Learning in Texture Classification., , and . ICIAR (1), volume 6753 of Lecture Notes in Computer Science, page 335-343. Springer, (2011)Semi-supervised Dictionary Learning Based on Hilbert-Schmidt Independence Criterion., , , and . ICIAR, volume 9730 of Lecture Notes in Computer Science, page 12-19. Springer, (2016)Nonlinear Scale Space Theory in Texture Classification Using Multiple Classifier Systems., , and . ICIAR (1), volume 6111 of Lecture Notes in Computer Science, page 147-156. Springer, (2010)Triaging Diagnostically Relevant Regions from Pathology Whole Slides of Breast Cancer: A Texture Based Approach., , , , and . IEEE Trans. Medical Imaging, 35 (1): 307-315 (2016)Supervised Texture Classification Using a Novel Compression-Based Similarity Measure., , and . ICCVG, volume 7594 of Lecture Notes in Computer Science, page 379-386. Springer, (2012)