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Applying curriculum learning requires both a range of difficulty in data anda method for determining the difficulty of examples. In many tasks, however,satisfying these requirements can be a formidable challenge. In this paper, wecontend that histopathology image classification is a compelling use case forcurriculum learning. Based on the nature of histopathology images, a range ofdifficulty inherently exists among examples, and, since medical datasets areoften labeled by multiple annotators, annotator agreement can be used as anatural proxy for the difficulty of a given example. Hence, we propose a simplecurriculum learning method that trains on progressively-harder images asdetermined by annotator agreement. We evaluate our hypothesis on thechallenging and clinically-important task of colorectal polyp classification.Whereas vanilla training achieves an AUC of 83.7% for this task, a modeltrained with our proposed curriculum learning approach achieves an AUC of88.2%, an improvement of 4.5%. Our work aims to inspire researchers to thinkmore creatively and rigorously when choosing contexts for applying curriculumlearning.

Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification