EndoFSA：基于秩约束参数自适应的内窥镜少样本图像生成
EndoFSA: Endoscopic Few-Shot Image Generation via Rank-Constrained Parameter Adaptation
浏览论文内容
中文总结 AI 辅助
EndoFSA提出一种基于GAN的秩约束参数自适应方法，利用正常数据预训练生成器，仅更新少量参数以适应异常域，在无需像素级标注的情况下生成逼真异常图像，并达到与真实图像相当的下游分类性能。
中文摘要 AI 辅助
无线胶囊内镜（WCE）产生大规模胃肠道图像数据，然而病理发现仍然显著代表性不足，限制了基于深度学习的异常检测系统的泛化性能。基于合成数据生成（SDG）方法提供了一种缓解这种不平衡的实用解决方案。然而，它们直接在稀缺的异常样本上训练往往会导致不稳定性、过拟合和结构失真。解决这些挑战需要受控的自适应机制，在保留解剖学先验的同时实现逼真的病理变异。本文提出了EndoFSA，一种基于生成对抗网络（GAN）的模型，用于在WCE成像中通过自适应进行内窥镜少样本图像生成。EndoFSA利用在大量正常数据上预训练的生成器，并通过秩约束参数自适应，仅更新少量调制参数而保持预训练权重冻结，从而使用有限数量的训练样本将其适应到异常域。通过将参数更新限制在低维子空间，并结合感知边界正则化和簇级多样性控制，EndoFSA能够在有限数据条件下实现高效的模型自适应，缓解模式崩溃，同时保留从正常数据中学习到的解剖学先验。重要的是，EndoFSA无需像素级标注、掩膜或边界框监督即可运行。在涵盖各种异常类别的公开WCE基准数据集上的评估表明，EndoFSA生成的异常图像再现了真实病变形态。此外，在下游分类任务中，仅使用EndoFSA生成的合成异常图像训练图像分类器，其性能与使用真实图像训练的分类器相当。
英文摘要
WCE produces large-scale gastrointestinal image data yet pathological findings remain significantly underrepresented limiting the generalization performance of deep-learning based abnormality detection systems. SDG methods offer a practical solution to mitigate this imbalance. However their training directly on scarce abnormal samples often results in instability overfitting and structural distortions. Addressing these challenges requires controlled adaptation mechanisms that preserve anatomical priors while enabling realistic pathological variation. This paper presents EndoFSA a GAN-based model for Endoscopic Few-Shot image generation by Adaptation in WCE imaging. EndoFSA leverages a generator pretrained on abundant normal data and adapts it to abnormal domains using limited number of training samples through a rank-constrained parameter adaptation where only a small number of modulation parameters is updated while the pretrained weights remain frozen. By restricting parameter updates to a low dimensional subspace and incorporating perceptual boundary regularization and cluster-wise diversity control EndoFSA enables efficient model adaptation under limited data conditions and mitigates mode collapse while preserving the anatomical priors learned from normal data. Importantly EndoFSA operates without requiring pixel-level annotations, masks or bounding box supervision. Evaluation on publicly available WCE benchmark datasets spanning various abnormal categories demonstrates that EndoFSA generates abnormal images reproducing real lesions morphology. Moreover in a downstream classification task training an image classifier solely on synthetic abnormal images generated by EndoFSA yields performance comparable to that obtained with real images.
发表机构
- University of Thessaly（塞萨利大学）
机构由 AI 辅助整理，请以论文原文为准。
