Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

Vision Transformers (ViT)s have shown great performance in self-supervised learning of global and local representations that can be transferred to downstream applications. Our model is currently the advanced on the public test leaderboards of both MSD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://decathlon-10.grand-challenge.org/e...

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• 1 Inspired by these results, we introduce a novel self-supervised learning framework with tailored proxy tasks for medical image analysis.
• 2 Specifically, we propose: (i) a new 3D transformer-based model, dubbed Swin UNEt TRansformers (Swin UNETR), with a hierarchical encoder for self-supervised pretraining; (ii) tailored proxy tasks for learning the underlying pattern of human anatomy.
• 3 We demonstrate successful pre-training of the proposed model on 5,050 publicly available computed tomography (CT) images from various body organs.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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