-
[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Wei_2021_WACV, author = {Wei, Jerry and Suriawinata, Arief and Ren, Bing and Liu, Xiaoying and Lisovsky, Mikhail and Vaickus, Louis and Brown, Charles and Baker, Michael and Nasir-Moin, Mustafa and Tomita, Naofumi and Torresani, Lorenzo and Wei, Jason and Hassanpour, Saeed}, title = {Learn Like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2021}, pages = {2473-2483} }
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.
Related Material