发表机构
Institute of Automation, Chinese Academy of Sciences; Nanyang Technological University（中国科学院自动化研究所; 南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对医学成像中广义类别发现存在的旧类偏差问题，提出MedXplore框架，通过FAAC与ACAM模块优化表征学习，在多基准上实现准确率提升且旧类误报率显著降低。
AI 中文摘要
深度学习在医学图像分析中展现出强大潜力，但现有多数方法依赖大规模标注及临床实践中极少成立的闭世界假设。尽管广义类别发现（GCD）在自然图像领域已发展迅速，其在医学成像中的探索仍不充分。为解决该问题，本文提出MedXplore——一个用于可靠且无偏医学GCD的统一框架，从感知与决策两个层面进行优化。具体而言，在感知层面，基于频域视角，频域信噪比自适应注意力与一致性（FAAC）执行可学习的全频谱滤波及全局-局部能量对比激活，既突出相对于全局上下文的局部异常信号，又为 patch 一致性学习提供可靠语义锚点；在决策层面，自适应余弦-角度间隔（ACAM）利用语义难度与特征置信度调整角度间隔，以平衡类内紧凑性与类间可分性。上述两个模块协同改进病灶敏感的表征学习并缓解旧类偏差。在多个基准上的实验显示，相较于最强的竞争方法，MedXplore在All准确率上平均提升8.5%；在Kvasir数据集上，MedXplore将旧类误报率从14.50%降至0.80%，在严重的新旧类别模糊性下展现出强鲁棒性。
英文摘要
Deep learning has shown strong potential in medical image analysis, but most existing methods rely on large-scale annotations and a closed-world assumption that rarely holds in clinical practice. Although Generalized Category Discovery (GCD) has advanced rapidly on natural images, it remains underexplored in medical imaging. To address this issue, we propose MedXplore, a unified framework for reliable and unbiased medical GCD, optimizing from both perceptual and decision levels. Specifically, at the perceptual level, taking a frequency domain perspective, Frequency-SNR Adaptive Attention and Consistency (FAAC) performs learnable full-spectrum filtering and global-local energy contrast activation to not only highlight local abnormal signals relative to the global context, but also provide reliable semantic anchors for patch consistency learning. At the decision level, Adaptive Cosine-Angular Margin (ACAM) adjusts angular margins using semantic difficulty and feature confidence to balance intra-class compactness and inter-class separability. Together, the two modules improve lesion-sensitive representation learning and mitigate old-class bias. Experiments on multiple benchmarks show an average \textbf{8.5\%} gain in \textit{All} accuracy over the strongest competing methods. On Kvasir, MedXplore reduces false-old errors from 14.50\% to 0.80\%, demonstrating strong robustness under severe old-new ambiguity.
Commentsaccepted by ACM MM 26
