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

Abstract

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.

References in corpus (5)

• Automated Gleason Grading of Prostate Biopsies using Deep Learning
• On The Power of Curriculum Learning in Training Deep Networks
• Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence
• Rethinking Curriculum Learning with Incremental Labels and Adaptive Compensation
• Difficulty Translation in Histopathology Images

Cited by in corpus (1)

• A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis