对组织学兴趣区域的鉴定和切割可以为病理学家在诊断任务中提供重要支持。然而，切割方法受到了在获取像素级注释方面的困难的限制，这些注释对于全切片图像（WSI）来说是耗时且昂贵的。尽管已经开发了几种方法来利用图像级弱监督进行 WSI 分类，但使用 WSI 级别标签进行切割任务却受到了很少的关注。这个研究方向通常需要除图像标签以外的额外监督，而在实际世界实践中很难获得这些监督。在本研究中，我们提出了一种名为 WholeSIGHT 的弱监督方法，可以同时对任意形状和大小的 WSI 进行切割和分类。WholeSIGHT 首先构建 WSI 的组织图谱表示，其中节点和边分别表示组织区域及其交互。在训练过程中，一个图分类头部对 WSI 进行分类，并通过事后特征归因产生节点级伪标签。然后利用这些伪标签来训练用于 WSI 切割的节点分类头部。在测试过程中，两个头部同时为输入的 WSI 绘制切割和类别预测。我们在三个公共前列腺癌 WSI 数据集上评估了 WholeSIGHT 的性能。我们的方法在所有数据集上都实现了最先进的弱监督切割性能，并在与最先进的弱监督 WSI 分类方法相比方面获得了更好或可比的分类结果。此外，我们还评估了我们方法在切割和分类性能、不确定性估计和模型校准方面的泛化能力。我们的代码可在以下链接获取：https://github.com/histocartography/wholesight。版权所有 © 2023 Elsevier B.V.

The identification and segmentation of histological regions of interest can provide significant support to pathologists in their diagnostic tasks. However, segmentation methods are constrained by the difficulty in obtaining pixel-level annotations, which are tedious and expensive to collect for whole-slide images (WSI). Though several methods have been developed to exploit image-level weak-supervision for WSI classification, the task of segmentation using WSI-level labels has received very little attention. The research in this direction typically require additional supervision beyond image labels, which are difficult to obtain in real-world practice. In this study, we propose WholeSIGHT, a weakly-supervised method that can simultaneously segment and classify WSIs of arbitrary shapes and sizes. Formally, WholeSIGHT first constructs a tissue-graph representation of WSI, where the nodes and edges depict tissue regions and their interactions, respectively. During training, a graph classification head classifies the WSI and produces node-level pseudo-labels via post-hoc feature attribution. These pseudo-labels are then used to train a node classification head for WSI segmentation. During testing, both heads simultaneously render segmentation and class prediction for an input WSI. We evaluate the performance of WholeSIGHT on three public prostate cancer WSI datasets. Our method achieves state-of-the-art weakly-supervised segmentation performance on all datasets while resulting in better or comparable classification with respect to state-of-the-art weakly-supervised WSI classification methods. Additionally, we assess the generalization capability of our method in terms of segmentation and classification performance, uncertainty estimation, and model calibration. Our code is available at: https://github.com/histocartography/wholesight.Copyright © 2023 Elsevier B.V. All rights reserved.