让医疗影像模型识别未知疾病，提升内镜AI安全性
Open Set Recognition for Endoscopic Image Classification: A Deep Learning Approach on the Kvasir Dataset
- 用OpenMax方法测试多种模型在未知病灶下的识别能力
- ResNet-50与Swin Transformer在开放集下仍保持较高准确率
- 为内镜AI部署提供真实临床场景的评估基准
内镜图像分类在医学诊断中至关重要，用于识别解剖标志和病理发现。然而，传统闭集分类框架在开放世界临床环境中存在固有局限，因可能遇到未见过的病症而影响模型可靠性。为此，本文首次将开放集识别（OSR）技术应用于公开且多样的Kvasir数据集，评估并比较了ResNet-50、Swin Transformer及混合型ResNet-Transformer模型在闭集与开集条件下的表现。以OpenMax作为基线方法，检验模型区分已知类别与未知类别的能力。本研究为医疗图像分析中的OSR性能评估提供了基础基准，揭示了模型在真实临床环境中的行为特征，强调了OSR技术在内镜AI安全部署中的关键作用。
原文摘要 · Abstract (English)
Endoscopic image classification plays a pivotal role in medical diagnostics by identifying anatomical landmarks and pathological findings. However, conventional closed-set classification frameworks are inherently limited in open-world clinical settings, where previously unseen conditions can arise andcompromise model reliability. To address this, we explore the application of Open Set Recognition (OSR) techniques on the Kvasir dataset, a publicly available and diverse endoscopic image collection. In this study, we evaluate and compare the OSR capabilities of several representative deep learning architectures, including ResNet-50, Swin Transformer, and a hybrid ResNet-Transformer model, under both closed-set and open-set conditions. OpenMax is adopted as a baseline OSR method to assess the ability of these models to distinguish known classes from previously unseen categories. This work represents one of the first efforts to apply open set recognition to the Kvasir dataset and provides a foundational benchmark for evaluating OSR performance in medical image analysis. Our results offer practical insights into model behavior in clinically realistic settings and highlight the importance of OSR techniques for the safe deployment of AI systems in endoscopy.
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