用扩散模型提升骨肉瘤坏死评估，分割精度提高10%。
Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models
- 分两阶段处理：先分类后精修，融合多切片信息
- mIOU提升10%，坏死率估算准确率提高32.12%
- 适合医学影像分析、病理图像分割的研究者
骨肉瘤是最常见的原发性骨癌，准确评估全切片图像（WSIs）中的坏死程度对治疗方案制定和预后判断至关重要。然而人工评估主观性强，结果差异大。为此，我们提出FDDM框架，连接切片分类与区域分割任务。该框架分两阶段运行：先进行切片级分类，再进行区域级精修，实现跨切片信息融合。基于新构建的骨肉瘤图像数据集，FDDM在分割性能上超越现有方法，mIOU最高提升10%，坏死率估算准确率提高32.12%。该框架为骨肉瘤评估设立了新基准，展现了基础模型与扩散模型在复杂医学影像任务中的潜力。
原文摘要 · Abstract (English)
Osteosarcoma, the most common primary bone cancer, often requires accurate necrosis assessment from whole slide images (WSIs) for effective treatment planning and prognosis. However, manual assessments are subjective and prone to variability. In response, we introduce FDDM, a novel framework bridging the gap between patch classification and region-based segmentation. FDDM operates in two stages: patch-based classification, followed by region-based refinement, enabling cross-patch information intergation. Leveraging a newly curated dataset of osteosarcoma images, FDDM demonstrates superior segmentation performance, achieving up to a 10% improvement mIOU and a 32.12% enhancement in necrosis rate estimation over state-of-the-art methods. This framework sets a new benchmark in osteosarcoma assessment, highlighting the potential of foundation models and diffusion-based refinements in complex medical imaging tasks.
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