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:this https URL
View on arXiv@article{shi2025_2502.10608,
title={ Universal Lesion Segmentation Challenge 2023: A Comparative Research of Different Algorithms },
author={ Kaiwen Shi and Yifei Li and Binh Ho and Jovian Wang and Kobe Guo },
journal={arXiv preprint arXiv:2502.10608},
year={ 2025 }
}