Polyp-E：通过息肉编辑基准测试深度分割模型的鲁棒性
Polyp-E: Benchmarking the Robustness of Deep Segmentation Models via Polyp Editing
AI总结：
本文提出Polyp-E基准，利用Latent Diffusion Model对真实息肉进行属性编辑生成高真实感数据，评估并揭示了深度分割模型对息肉位置和大小变化高度敏感，同时该编辑流程可作为数据增强提升模型泛化能力。
摘要：
自动息肉分割有助于辅助临床诊断和治疗。在日常临床实践中，临床医生在识别位置和大小变化的息肉时表现出鲁棒性。目前尚不确定深度分割模型能否在自动化结肠镜分析中达到可比的鲁棒性。为了基准测试模型鲁棒性，我们重点评估分割模型在具有各种属性（例如位置和大小）的息肉以及健康样本上的鲁棒性。基于Latent Diffusion Model，我们对真实息肉进行属性编辑，并构建了一个名为Polyp-E的新数据集。我们的合成数据集具有出色的真实感，以至于临床专家发现很难将其与真实数据区分开来。我们在所提出的基准上评估了几种现有的息肉分割模型。结果表明，大多数模型对属性变化高度敏感。作为一种新颖的数据增强技术，所提出的编辑流程可以提高分布内和分布外的泛化能力。代码和数据集将发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
