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Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography.

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Effect of different molecular subtype reference standards in AI training: implications for DCE-MRI radiomics of breast cancers., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)Complementary expert balanced learning for long-tail cross-modal retrieval., and . Multim. Syst., 30 (2): 113 (April 2024)Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography., , , , , and . CoRR, (2020)Evaluating deep learning techniques for dynamic contrast-enhanced MRI in the diagnosis of breast cancer., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10950 of SPIE Proceedings, page 1095006. SPIE, (2019)Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography., , , , , and . MICCAI (6), volume 11769 of Lecture Notes in Computer Science, page 468-476. Springer, (2019)Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2, 397 breast lesions., , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10950 of SPIE Proceedings, page 109501A. SPIE, (2019)Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Method., , , , and . Proc. IEEE, 108 (1): 163-177 (2020)