Learning Better Contrastive View from Radiologist's Gaze

• URL: http://arxiv.org/abs/2305.08826v1
• Date: Mon, 15 May 2023 17:34:49 GMT
• Title: Learning Better Contrastive View from Radiologist's Gaze
• Authors: Sheng Wang, Zixu Zhuang, Xi Ouyang, Lichi Zhang, Zheren Li, Chong Ma, Tianming Liu, Dinggang Shen, Qian Wang
• Abstract summary: We propose a novel augmentation method, i.e., FocusContrast, to learn from radiologists' gaze in diagnosis and generate contrastive views for medical images. Specifically, we track the gaze movement of radiologists and model their visual attention when reading to diagnose X-ray images. As a plug-and-play module, FocusContrast consistently improves state-of-the-art contrastive learning methods of SimCLR, MoCo, and BYOL by 4.07.0% in classification accuracy on a knee X-ray dataset.
• Score: 45.55702035003462
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims to minimizing distances between positive pairs. These methods usually apply random data augmentation to input images, expecting the augmented views of the same images to be similar and positively paired. However, random augmentation may overlook image semantic information and degrade the quality of augmented views in contrastive learning. This issue becomes more challenging in medical images since the abnormalities related to diseases can be tiny, and are easy to be corrupted (e.g., being cropped out) in the current scheme of random augmentation. In this work, we first demonstrate that, for widely-used X-ray images, the conventional augmentation prevalent in contrastive pre-training can affect the performance of the downstream diagnosis or classification tasks. Then, we propose a novel augmentation method, i.e., FocusContrast, to learn from radiologists' gaze in diagnosis and generate contrastive views for medical images with guidance from radiologists' visual attention. Specifically, we track the gaze movement of radiologists and model their visual attention when reading to diagnose X-ray images. The learned model can predict visual attention of the radiologists given a new input image, and further guide the attention-aware augmentation that hardly neglects the disease-related abnormalities. As a plug-and-play and framework-agnostic module, FocusContrast consistently improves state-of-the-art contrastive learning methods of SimCLR, MoCo, and BYOL by 4.0~7.0% in classification accuracy on a knee X-ray dataset.

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