“无需阴性样本”：用于全切片组织病理学图像中可解释肿瘤检测的弱监督回归
"No negatives needed": weakly-supervised regression for interpretable tumor detection in whole-slide histopathology images
AI总结：
本文提出一种无需阴性样本的弱监督回归框架，通过估计肿瘤百分比实现全切片图像中的可解释肿瘤检测，并引入放大技术提升小肿瘤区域检测灵敏度。
摘要：
在数字病理学全切片图像（WSIs）中准确检测肿瘤对于癌症诊断和治疗计划至关重要。多实例学习（MIL）已成为一种广泛使用的方法，用于在大规模数据下进行弱监督肿瘤检测，无需手动标注。然而，传统的MIL方法通常依赖于分类任务，需要无肿瘤病例作为阴性样本，这在真实临床工作流程中难以获得，尤其是对于手术切除标本。我们通过将肿瘤检测重新构建为回归任务来解决这一限制，从WSIs中估计肿瘤百分比，这是一个在多种癌症类型中临床可用的目标。在本文中，我们通过将所提出的弱监督回归框架应用于多个器官、标本类型和临床场景，对其进行了分析。我们表征了该框架对肿瘤百分比作为噪声回归目标的鲁棒性，并引入了一种新颖的放大技术概念，以提高从小肿瘤区域学习时的肿瘤检测灵敏度。最后，我们通过分析视觉注意力和logit图，为模型的预测提供了可解释的见解。我们的代码可在https://github.com/DIAGNijmegen/tumor-percentage-mil-regression获取。
英文摘要：
Accurate tumor detection in digital pathology whole-slide images (WSIs) is crucial for cancer diagnosis and treatment planning. Multiple Instance Learning (MIL) has emerged as a widely used approach for weakly-supervised tumor detection with large-scale data without the need for manual annotations. However, traditional MIL methods often depend on classification tasks that require tumor-free cases as negative examples, which are challenging to obtain in real-world clinical workflows, especially for surgical resection specimens. We address this limitation by reformulating tumor detection as a regression task, estimating tumor percentages from WSIs, a clinically available target across multiple cancer types. In this paper, we provide an analysis of the proposed weakly-supervised regression framework by applying it to multiple organs, specimen types and clinical scenarios. We characterize the robustness of our framework to tumor percentage as a noisy regression target, and introduce a novel concept of amplification technique to improve tumor detection sensitivity when learning from small tumor regions. Finally, we provide interpretable insights into the model's predictions by analyzing visual attention and logit maps. Our code is available at https://github.com/DIAGNijmegen/tumor-percentage-mil-regression.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
