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4 April 2022 Deep curriculum learning in task space for multi-class based mammography diagnosis
Jun Luo, Dooman Arefan, Margarita Zuley M.D., Jules Sumkin, Shandong Wu
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Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120330C (2022) https://doi.org/10.1117/12.2612617
Event: SPIE Medical Imaging, 2022, San Diego, California, United States
Abstract
Mammography is used as a standard screening procedure for the potential patients of breast cancer. Over the past decade, it has been shown that deep learning techniques have succeeded in reaching near-human performance in a number of tasks, and its application in mammography is one of the topics that medical researchers most concentrate on. In this work, we propose an end-to-end Curriculum Learning (CL) strategy in task space for classifying the three categories of Full-Field Digital Mammography (FFDM), namely Malignant, Negative, and False recall. Specifically, our method treats this three-class classification as a “harder” task in terms of CL, and creates an “easier” sub-task of classifying False recall against the combined group of Negative and Malignant. We introduce a loss scheduler to dynamically weight the contribution of the losses from the two tasks throughout the entire training process. We conduct experiments on an FFDM dataset of 1,709 images using 5-fold cross validation. The results show that our curriculum learning strategy can boost the performance for classifying the three categories of FFDM compared to the baseline strategies for model training.
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Jun Luo, Dooman Arefan, Margarita Zuley M.D., Jules Sumkin, and Shandong Wu "Deep curriculum learning in task space for multi-class based mammography diagnosis", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120330C (4 April 2022); https://doi.org/10.1117/12.2612617
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KEYWORDS
Mammography
Digital mammography
Artificial intelligence
Machine learning
Medical research
Image classification
Medical imaging
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