Related papers: A foundation model for generalizable disease diagnosis in chest X-ray images

A foundation model for generalizable disease diagnosis in chest X-ray images

URL: http://arxiv.org/abs/2410.08861v1
Date: Fri, 11 Oct 2024 14:41:27 GMT
Title: A foundation model for generalizable disease diagnosis in chest X-ray images
Authors: Lijian Xu, Ziyu Ni, Hao Sun, Hongsheng Li, Shaoting Zhang,
Abstract summary: We introduce CXRBase, a foundational model designed to learn versatile representations from unlabelled CXR images. CXRBase is trained on a substantial dataset of 1.04 million unlabelled CXR images. It is fine-tuned with labeled data to enhance its performance in disease detection.
Score: 40.9095393430871
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Medical artificial intelligence (AI) is revolutionizing the interpretation of chest X-ray (CXR) images by providing robust tools for disease diagnosis. However, the effectiveness of these AI models is often limited by their reliance on large amounts of task-specific labeled data and their inability to generalize across diverse clinical settings. To address these challenges, we introduce CXRBase, a foundational model designed to learn versatile representations from unlabelled CXR images, facilitating efficient adaptation to various clinical tasks. CXRBase is initially trained on a substantial dataset of 1.04 million unlabelled CXR images using self-supervised learning methods. This approach allows the model to discern meaningful patterns without the need for explicit labels. After this initial phase, CXRBase is fine-tuned with labeled data to enhance its performance in disease detection, enabling accurate classification of chest diseases. CXRBase provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from chest imaging.

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