Pre-training on High Definition X-ray Images: An Experimental Study

TL;DR

This paper introduces a high-resolution, large-scale X-ray pre-trained vision model using a novel masking strategy, significantly improving performance on report generation and disease recognition tasks.

Contribution

It presents the first high-definition X-ray pre-trained model on over 1 million images with a novel context-aware masking strategy for better downstream task performance.

Findings

Achieves state-of-the-art results on benchmark datasets.

Demonstrates effectiveness of high-resolution pre-training.

Validates the proposed masking strategy improves model learning.

Abstract

Existing X-ray based pre-trained vision models are usually conducted on a relatively small-scale dataset (less than 500k samples) with limited resolution (e.g., 224 $\times$ 224). However, the key to the success of self-supervised pre-training large models lies in massive training data, and maintaining high resolution in the field of X-ray images is the guarantee of effective solutions to difficult miscellaneous diseases. In this paper, we address these issues by proposing the first high-definition (1280 $\times$ 1280) X-ray based pre-trained foundation vision model on our newly collected large-scale dataset which contains more than 1 million X-ray images. Our model follows the masked auto-encoder framework which takes the tokens after mask processing (with a high rate) is used as input, and the masked image patches are reconstructed by the Transformer encoder-decoder network. More importantly, we introduce a novel context-aware masking strategy that utilizes the chest contour as a boundary for adaptive masking operations. We validate the effectiveness of our model on two downstream tasks, including X-ray report generation and disease recognition. Extensive experiments demonstrate that our pre-trained medical foundation vision model achieves comparable or even new state-of-the-art performance on downstream benchmark datasets. The source code and pre-trained models of this paper will be released on https://github.com/Event-AHU/Medical_Image_Analysis.