用扩散Transformer提升皮肤病变分割精度与速度
SegDT: A Diffusion Transformer-Based Segmentation Model for Medical Imaging
- 基于扩散Transformer架构，结合修正流提升生成质量
- 在三个数据集上达顶尖性能，推理速度显著加快
- 适合资源受限环境下的医疗影像实时诊断
医学图像分割对疾病诊断和治疗规划至关重要，尤其在皮肤病变分割方面，对皮肤癌诊断和患者监测具有重要意义。本文提出一种基于扩散Transformer（DiT）的新型分割模型SegDT，专为低成本硬件设计，并引入修正流（Rectified Flow），在减少推理步数的同时提升生成质量，同时保持标准扩散模型的灵活性。该方法在三个基准数据集上进行评估，相比现有方法取得领先性能，且推理速度更快。结果表明，该模型可有效提升深度学习在医疗影像分析中的表现，为临床医生提供更快速、更准确的诊断工具。代码已公开于GitHub。
原文摘要 · Abstract (English)
Medical image segmentation is crucial for many healthcare tasks, including disease diagnosis and treatment planning. One key area is the segmentation of skin lesions, which is vital for diagnosing skin cancer and monitoring patients. In this context, this paper introduces SegDT, a new segmentation model based on diffusion transformer (DiT). SegDT is designed to work on low-cost hardware and incorporates Rectified Flow, which improves the generation quality at reduced inference steps and maintains the flexibility of standard diffusion models. Our method is evaluated on three benchmarking datasets and compared against several existing works, achieving state-of-the-art results while maintaining fast inference speeds. This makes the proposed model appealing for real-world medical applications. This work advances the performance and capabilities of deep learning models in medical image analysis, enabling faster, more accurate diagnostic tools for healthcare professionals. The code is made publicly available at \href{https://github.com/Bekhouche/SegDT}{GitHub}.
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