用编辑真实息肉构建新数据集，测试分割模型鲁棒性
Polyp-E: Benchmarking the Robustness of Deep Segmentation Models via Polyp Editing
- 基于扩散模型编辑真实息肉，生成高保真合成数据
- 多数模型对息肉位置大小变化敏感，泛化能力差
- 可提升模型在分布内和分布外的分割性能
自动息肉分割有助于临床诊断与治疗。日常诊疗中，医生能稳健识别不同位置和尺寸的息肉，但深度分割模型是否具备同等鲁棒性尚不明确。为此，本文聚焦于评估模型在不同属性（如位置、尺寸）息肉及健康样本上的鲁棒性。基于潜空间扩散模型，我们对真实息肉进行属性编辑，构建了名为Polyp-E的新数据集。该合成数据集具有极高的真实感，临床专家难以区分其与真实数据。我们在该基准上评估了多个现有息肉分割模型，结果表明大多数模型对属性变化高度敏感。作为新型数据增强方法，所提出的编辑流程可有效提升模型在分布内和分布外场景下的泛化能力。代码与数据集将公开。
原文摘要 · Abstract (English)
Automatic polyp segmentation is helpful to assist clinical diagnosis and treatment. In daily clinical practice, clinicians exhibit robustness in identifying polyps with both location and size variations. It is uncertain if deep segmentation models can achieve comparable robustness in automated colonoscopic analysis. To benchmark the model robustness, we focus on evaluating the robustness of segmentation models on the polyps with various attributes (e.g. location and size) and healthy samples. Based on the Latent Diffusion Model, we perform attribute editing on real polyps and build a new dataset named Polyp-E. Our synthetic dataset boasts exceptional realism, to the extent that clinical experts find it challenging to discern them from real data. We evaluate several existing polyp segmentation models on the proposed benchmark. The results reveal most of the models are highly sensitive to attribute variations. As a novel data augmentation technique, the proposed editing pipeline can improve both in-distribution and out-of-distribution generalization ability. The code and datasets will be released.
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