用于病理预测交互细化的条件随机场
Conditional Random Fields for Interactive Refinement of Histopathological Predictions
AI总结：
本文提出HistoCRF框架，通过条件随机场细化病理预测，利用专家注释提升分类准确率，实验显示在无注释和少量注释情况下均取得显著提升。
摘要：
协助病理科医生分析病理图像具有很高的临床价值，因为这支持癌症的检测和分期。在此背景下，病理学基础模型最近已出现。其中，视觉-语言模型（VLMs）提供了强大但不完美的零样本预测。我们提出通过将条件随机场（CRFs）适应到病理学应用中来细化这些预测，不需要额外的模型训练。我们提出了HistoCRF，一个基于CRF的框架，其新颖的二元势函数促进标签多样性并利用专家注释。我们考虑了三个实验：无注释、有专家注释以及有迭代的人工循环注释，后者逐步纠正误分类的块。在五个涵盖不同器官和疾病的块级分类数据集上的实验表明，与零样本预测相比，无注释时平均准确率提高了16.0%，仅使用100个注释时提高了27.5%。此外，整合人类循环可进一步提高32.6%，使用相同数量的注释。代码将在https://github.com/tgodelaine/HistoCRF上提供。
英文摘要：
Assisting pathologists in the analysis of histopathological images has high clinical value, as it supports cancer detection and staging. In this context, histology foundation models have recently emerged. Among them, Vision-Language Models (VLMs) provide strong yet imperfect zero-shot predictions. We propose to refine these predictions by adapting Conditional Random Fields (CRFs) to histopathological applications, requiring no additional model training. We present HistoCRF, a CRF-based framework, with a novel definition of the pairwise potential that promotes label diversity and leverages expert annotations. We consider three experiments: without annotations, with expert annotations, and with iterative human-in-the-loop annotations that progressively correct misclassified patches. Experiments on five patch-level classification datasets covering different organs and diseases demonstrate average accuracy gains of 16.0% without annotations and 27.5% with only 100 annotations, compared to zero-shot predictions. Moreover, integrating a human in the loop reaches a further gain of 32.6% with the same number of annotations. The code will be made available on https://github.com/tgodelaine/HistoCRF.
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