发表机构
Université Catholique de Louvain（鲁汶天主教大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对全切片图像分类，提出SlideTIM方法，结合空间-潜在正则化与类别分布先验，在四个组织学数据集上显著提升直推式少样本分类性能。
AI 中文摘要
自动化分析全切片图像（WSIs）是癌症诊断中的关键步骤，具有很高的临床价值，因为它可以减轻病理学家的工作负担，同时提高诊断准确性。近年来，视觉-语言模型在无需任何标注的情况下展现出良好的补丁级分类性能，然而这些零样本（ZS）预测在细粒度任务上仍然存在噪声，需要进一步细化。一个有前景的方向是联合细化所有预测，即采用直推式方法。然而，大多数现有方法并非针对WSIs量身定制。因此，我们提出了SlideTIM，这是对近期直推式方法LC-TIM的WSIs适应性改进，该方法引入了结合空间-潜在正则化器以及补丁类别分布先验。前者强制空间和语义上接近的补丁获得相同的预测，而先验则校准预测的类别比例。两者共同应对WSIs复杂的空间组织和强烈的类别不平衡问题。在四个组织学数据集上的评估中，SlideTIM始终优于所有TIM变体，在1-shot设置下，相较于最佳竞争基线，宏F1分数提高了+8.1个百分点。与ZS相比，在1-shot设置下，宏F1分数提高了+19.4个百分点。代码将在提交后公开。
英文摘要
Automating the analysis of whole-slide images (WSIs), a key step in cancer diagnosis, has high clinical value, as it can reduce pathologist's workload while improving diagnosis accuracy. Recently, vision-language models have shown promising performance for patch-level classification without requiring any annotation, yet these zero-shot (ZS) predictions remain noisy on fine-grained tasks and must be further refined. A promising direction is to refine all predictions jointly, i.e., a transductive approach. However, most existing methods are not tailored to WSIs. We thus propose SlideTIM, an adaptation to WSIs of the recent transductive approach LC-TIM, which introduces a combined spatial--latent regularizer together with a prior on the patch class distribution. The former enforces spatially and semantically close patches to receive the same predictions, while the prior calibrates the predicted class proportions. Together, they address the complex spatial organization and the strong class imbalance of WSIs. Evaluated on four histology datasets, SlideTIM consistently outperforms all TIM variants, improving the macro-F1 by +8.1pp over the best competing baseline at 1 shot. Compared to the ZS, it raises the macro-F1 by +19.4pp at 1 shot. The code will be made available after submission.
Comments5 pages, 2 figures
