Pre-training on high-resolution X-ray images:an experimental studyOA

Existing X-ray image based pre-trained vision models are typically trained on a relatively small-scale dataset(less than 500,000 samples)with limited resolution(e.g.,224×224).However,the key to the success of self-supervised pre-training of large models lies in massive training data,and the maintenance of high-resolution X-ray images contributes to effective solutions for some challenging diseases.In this paper,we proposed a high-resolution(1280×1280)X-ray image based pre-trained baseline model on our newly collected large-scale dataset containing more than 1 million X-ray images.Our model employs the masked auto-encoder framework,wherein the tokens that have been processed with a high rate are used as input,and the masked image patches are reconstructed by means of the Transformer encoder-decoder network.More importantly,a novel context-aware masking strategy has been introduced.This strategy utilizes the breast contour as a boundary for adaptive masking operations.We validate the effectiveness of our model through its application in two downstream tasks,namely X-ray report generation and disease detection.Extensive experiments demonstrate that our pre-trained medical baseline model can achieve comparable to,or even exceed,those of current state-of-the-art models on downstream benchmark datasets.