In recent years, deep learning models have revolutionized medical image interpretation, offering substantial improvements in diagnostic accuracy. However, these models often struggle with challenging images where critical features are partially or fully occluded, which is a common scenario in clinical practice. In this paper, we propose a novel curriculum learning-based approach to train deep learning models to handle occluded medical images effectively. Our method progressively introduces occlusion, starting from clear, unobstructed images and gradually moving to images with increasing occlusion levels. This ordered learning process, akin to human learning, allows the model to first grasp simple, discernable patterns and subsequently build upon this knowledge to understand more complicated, occluded scenarios. Furthermore, we present three novel occlusion synthesis methods, namely Wasserstein Curriculum Learning (WCL), Information Adaptive Learning (IAL), and Geodesic Curriculum Learning (GCL). Our extensive experiments on diverse medical image datasets demonstrate substantial improvements in model robustness and diagnostic accuracy over conventional training methodologies.

本研究提出了一种基于课程学习的深度学习模型，可有效处理被遮挡的医学图像，通过引入逐步递增的遮挡，模型可先掌握简单的图案再建立对更复杂遮挡情况的理解，并且所提出的三种遮挡综合方法证明了在医学图像的诊断精度和鲁棒性上相较于传统训练方法的明显提高。

医学影像中渐进遮挡的课程学习抵消雾霾影响