MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models

10/11/2020 ∙ by Hari Sowrirajan, et al. ∙ 4

Self-supervised approaches such as Momentum Contrast (MoCo) can leverage unlabeled data to produce pretrained models for subsequent fine-tuning on labeled data. While MoCo has demonstrated promising results on natural image classification tasks, its application to medical imaging tasks like chest X-ray interpretation has been limited. Chest X-ray interpretation is fundamentally different from natural image classification in ways that may limit the applicability of self-supervised approaches. In this work, we investigate whether MoCo-pretraining leads to better representations or initializations for chest X-ray interpretation. We conduct MoCo-pretraining on CheXpert, a large labeled dataset of X-rays, followed by supervised fine-tuning experiments on the pleural effusion task. Using 0.1 linear model trained on MoCo-pretrained representations outperforms one trained on representations without MoCo-pretraining by an AUC of 0.096 (95 0.130), indicating that MoCo-pretrained representations are of higher quality. Furthermore, a model fine-tuned end-to-end with MoCo-pretraining outperforms its non-MoCo-pretrained counterpart by an AUC of 0.037 (95 with the 0.1 fractions for both the linear model and an end-to-end fine-tuned model with the greater improvements for smaller label fractions. Finally, we observe similar results on a small, target chest X-ray dataset (Shenzhen dataset for tuberculosis) with MoCo-pretraining done on the source dataset (CheXpert), which suggests that pretraining on unlabeled X-rays can provide transfer learning benefits for a target task. Our study demonstrates that MoCo-pretraining provides high-quality representations and transferable initializations for chest X-ray interpretation.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.