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The Automated Learning of Deep Features for Breast Mass Classification from Mammograms.

, , and . MICCAI (2), volume 9901 of Lecture Notes in Computer Science, page 106-114. (2016)

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Deep Structured learning for mass segmentation from Mammograms., , and . CoRR, (2014)Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms., , and . Deep Learning and Convolutional Neural Networks for Medical Image Computing, Springer, (2017)Deep Residual Recurrent Neural Networks for Characterisation of Cardiac Cycle Phase from Echocardiograms., , , , , , , , , and . DLMIA/ML-CDS@MICCAI, volume 10553 of Lecture Notes in Computer Science, page 100-108. Springer, (2017)Cardiac Phase Detection in Echocardiograms With Densely Gated Recurrent Neural Networks and Global Extrema Loss., , , , , , , , , and 1 other author(s). IEEE Trans. Medical Imaging, 38 (8): 1821-1832 (2019)Multi-scale mass segmentation for mammograms via cascaded random forests., , , , and . ISBI, page 113-117. IEEE, (2017)Designing lightweight deep learning models for echocardiography view classification., , , , , , , , , and 2 other author(s). Medical Imaging: Image-Guided Procedures, volume 10951 of SPIE Proceedings, page 109510F. SPIE, (2019)A deep learning approach for the analysis of masses in mammograms with minimal user intervention., , and . Medical Image Anal., (2017)Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms., , and . ISBI, page 760-763. IEEE, (2015)The Automated Learning of Deep Features for Breast Mass Classification from Mammograms., , and . MICCAI (2), volume 9901 of Lecture Notes in Computer Science, page 106-114. (2016)Fully automated classification of mammograms using deep residual neural networks., , and . ISBI, page 310-314. IEEE, (2017)