Universal Lesion Segmentation Challenge 2023: A Comparative Research of Different Algorithms
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Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 14 Feb 2025.
Builds on
11 of 18 references.
References · 11
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Citing papers per year: 2026: 1.
1 paper.