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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)

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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)Multi-scale mass segmentation for mammograms via cascaded random forests., , , , and . ISBI, page 113-117. IEEE, (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)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)Fully automated classification of mammograms using deep residual neural networks., , and . ISBI, page 310-314. IEEE, (2017)Deep structured learning for mass segmentation from mammograms., , and . ICIP, page 2950-2954. IEEE, (2015)Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests., , and . DICTA, page 1-8. IEEE, (2015)Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms., , and . MICCAI (1), volume 9349 of Lecture Notes in Computer Science, page 605-612. Springer, (2015)Mass segmentation in mammograms: A cross-sensor comparison of deep and tailored features., , , , and . ICIP, page 1737-1741. IEEE, (2017)