Applying curriculum learning requires both a range of difficulty in data and a method for determining the difficulty of examples. In many tasks, however, satisfying these requirements can be a formidable challenge. In this paper, we contend that histopathology image classification is a compelling use case for curriculum learning. Based on the nature of histopathology images, a range of difficulty inherently exists among examples, and, since medical datasets are often labeled by multiple annotators, annotator agreement can be used as a natural proxy for the difficulty of a given example. Hence, we propose a simple curriculum learning method that trains on progressively-harder images as determined by annotator agreement. We evaluate our hypothesis on the challenging and clinically-important task of colorectal polyp classification. Whereas vanilla training achieves an AUC of 83.7% for this task, a model trained with our proposed curriculum learning approach achieves an AUC of 88.2%, an improvement of 4.5%. Our work aims to inspire researchers to think more creatively and rigorously when choosing contexts for applying curriculum learning.

该论文提出了一种简单的课程学习方法，用于根据标注者一致性评估的难度逐步训练组织病理学图像分类模型，在结直肠息肉分类任务中将模型性能从83.7%的AUC提高到88.2%的AUC，以启发研究人员在选择应用课程学习时更具创造性和严谨性。

像病理学家一样学习: 基于注释者一致性的组织病理学图片分类课程学习