论文标题
像病理学家一样学习：课程学习通过注释者协议组织病理学图像分类
Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification
论文作者
论文摘要
应用课程学习需要一系列难度数据，也需要确定示例难度的方法。但是，在许多任务中，满足这些要求可能是一个巨大的挑战。在本文中，我们认为组织病理学图像分类是课程学习的引人注目的用例。基于组织病理学图像的性质，示例之间存在一系列困难，并且由于医疗数据集通常被多个注释者标记，因此注释者一致性可以用作给定示例难度的自然代理。因此，我们提出了一种简单的课程学习方法，该方法通过注释者协议确定的逐步训练逐步训练。我们评估了关于结直肠息肉分类的具有挑战性和临床重要任务的假设。而香草培训为这项任务达到了83.7％的AUC，但通过我们建议的课程学习方法培训的模型可实现88.2％的AUC，提高了4.5％。我们的工作旨在激发研究人员在选择应用课程学习的环境时更具创造力和严格的思考。
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.
