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Deep learning of longitudinal mammogram examinations for breast cancer risk prediction.

, , , , , and . Pattern Recognit., (2022)

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Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning., and . CoRR, (2021)Fully-automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI by integrating a continuous max-flow model and a likelihood atlas., , , and . Medical Imaging: Computer-Aided Diagnosis, volume 8670 of SPIE Proceedings, page 86701C. SPIE, (2013)Pre-operative Microvascular Invasion Prediction Using Multi-parametric Liver MRI Radiomics., , , , and . J. Digit. Imaging, 33 (6): 1376-1386 (2020)Fedsld: Federated Learning with Shared Label Distribution for Medical Image Classification., and . ISBI, page 1-5. IEEE, (2022)PGFed: Personalize Each Client's Global Objective for Federated Learning., , , and . ICCV, page 3923-3933. IEEE, (2023)Action recognition in videos acquired by a moving camera using motion decomposition of Lagrangian particle trajectories., , and . ICCV, page 1419-1426. IEEE Computer Society, (2011)Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective., , , , and . J. Digital Imaging, 31 (4): 387-392 (2018)Probabilistic Cluster Signature for Modeling Motion Classes., , and . IROS, page 5731-5736. IEEE, (2009)Do pre-trained deep learning models improve computer-aided classification of digital mammograms?, , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 1057523. SPIE, (2018)Atlas-Based Probabilistic Fibroglandular Tissue Segmentation in Breast MRI., , and . MICCAI (2), volume 7511 of Lecture Notes in Computer Science, page 437-445. Springer, (2012)