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Mammography is used as a standard screening procedure for the potentialpatients of breast cancer. Over the past decade, it has been shown that deeplearning techniques have succeeded in reaching near-human performance in anumber of tasks, and its application in mammography is one of the topics thatmedical researchers most concentrate on. In this work, we propose an end-to-endCurriculum Learning (CL) strategy in task space for classifying the threecategories of Full-Field Digital Mammography (FFDM), namely Malignant,Negative, and False recall. Specifically, our method treats this three-classclassification as a "harder" task in terms of CL, and create an "easier"sub-task of classifying False recall against the combined group of Negative andMalignant. We introduce a loss scheduler to dynamically weight the contributionof the losses from the two tasks throughout the entire training process. Weconduct experiments on an FFDM datasets of 1,709 images using 5-fold crossvalidation. The results show that our curriculum learning strategy can boostthe performance for classifying the three categories of FFDM compared to thebaseline strategies for model training.

Deep Curriculum Learning in Task Space for Multi-Class Based Mammography Diagnosis