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

TL;DR

This paper introduces a novel curriculum learning approach in task space for classifying mammography images into three categories, improving accuracy over traditional training methods.

Contribution

It proposes an end-to-end curriculum learning strategy that dynamically weights sub-tasks to enhance multi-class mammography classification performance.

Findings

Improved classification accuracy on FFDM dataset.

Effective dynamic loss weighting strategy.

Enhanced model performance compared to baseline methods.

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 create 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 datasets 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.