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Evaluating deep learning techniques for dynamic contrast-enhanced MRI in the diagnosis of breast cancer.

, , , , и . Medical Imaging: Computer-Aided Diagnosis, том 10950 из SPIE Proceedings, стр. 1095006. SPIE, (2019)

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Peifang Zhang

Liu Liu

Liu Liu

Liu Liu

Lu Liu

 

Другие публикации лиц с тем же именем

Effect of different molecular subtype reference standards in AI training: implications for DCE-MRI radiomics of breast cancers., , , , и . Medical Imaging: Computer-Aided Diagnosis, том 12033 из SPIE Proceedings, SPIE, (2022)Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography., , , , , и . MICCAI (6), том 11769 из Lecture Notes in Computer Science, стр. 468-476. Springer, (2019)Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography., , , , , и . CoRR, (2020)Evaluating deep learning techniques for dynamic contrast-enhanced MRI in the diagnosis of breast cancer., , , , и . Medical Imaging: Computer-Aided Diagnosis, том 10950 из SPIE Proceedings, стр. 1095006. SPIE, (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., , , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 10950 из SPIE Proceedings, стр. 109501A. SPIE, (2019)Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Method., , , , и . Proc. IEEE, 108 (1): 163-177 (2020)