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arXiv · 2502.10608

Universal Lesion Segmentation Challenge 2023: A Comparative Research of Different Algorithms

Kaiwen Shi·Yifei Li·Binh Ho·Jovian Wang·Kobe Guo
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Abstract

In recent years, machine learning algorithms have achieved much success in segmenting lesions across various tissues. There is, however, not one satisfying model that works well on all tissue types universally. In response to this need, we attempt to train a model that 1) works well on all tissue types, and 2) is capable of still performing fast inferences. To this end, we design our architectures, test multiple existing architectures, compare their results, and settle upon SwinUnet. We document our rationales, successes, and failures. Finally, we propose some further directions that we think are worth exploring. codes: https://github.com/KWFredShi/ULS2023NGKD.git

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Kaiwen Shi, Yifei Li, Binh Ho, Jovian Wang, Kobe Guo. 2025-02-14. Universal Lesion Segmentation Challenge 2023: A Comparative Research of Different Algorithms. https://arxiv.org/abs/2502.10608

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