提出通用病灶分割模型，兼顾多组织类型表现与快速推理。
Universal Lesion Segmentation Challenge 2023: A Comparative Research of Different Algorithms
- 设计并测试多种架构，筛选出SwinUnet作为最优选择。
- 在多个组织类型上实现稳定分割性能，满足通用性要求。
- 适合医学图像分割研究者及需高效推理的临床应用开发。
近年来，机器学习算法在不同组织的病灶分割中取得了显著进展，但尚未出现能普遍适用于所有组织类型的理想模型。为应对这一挑战，我们致力于训练一个既能广泛适配各类组织、又具备快速推理能力的模型。为此，我们设计了多种网络架构，测试了多种现有架构，对比分析其效果，并最终选定SwinUnet作为最优方案。本文详细记录了设计逻辑、成功经验与失败教训，并提出了若干值得进一步探索的研究方向。
原文摘要 · Abstract (English)
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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