Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels
Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels
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DOI：
10.1016/j.media.2022.102487
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发表时间：
2022-06-04
影响因子：
10.9
通讯作者：
Liu, Zaiyi
中科院分区：
文献类型：
--
作者：
Han, Chu;Lin, Jiatai;Liu, Zaiyi
组织级语义分割是计算病理学的重要一步。全监督模型已经通过密集的像素级注释取得了出色的性能。然而，在千兆像素的整个幻灯片图像上绘制这样的标签是非常昂贵和耗时的。在本文中，我们仅使用块级分类标签来实现组织病理学图像的组织语义分割，最终减少注释工作。我们提出了一个两步模型，包括分类和分割阶段。在分类阶段，我们提出了一种基于 CAM 的模型，通过补丁级标签生成伪掩模。在分割阶段，我们通过提出的多层伪监督实现组织语义分割。已经提出了几种技术新颖性来减少像素级和块级注释之间的信息差距。作为本文的一部分，我们介绍了一种新的肺腺癌弱监督语义分割（WSSS）数据集（LUADHistoSeg）。我们进行了多次实验来评估我们在两个数据集上提出的模型。我们提出的模型优于五种最先进的 WSSS 方法。请注意，我们可以使用完全监督模型获得可比较的定量和定性结果，MIoU 和 FwIoU 的差距仅为 2% 左右。通过与随机采样的 100 个补丁数据集上的手动标记进行比较，补丁级标记可以将注释时间从几小时大大减少到几分钟。源代码和发布的数据集可在以下网址获取：https://github.com/ChuHan89/WSSS-Tissue。 (c) 2022 作者。由 Elsevier B.V 出版。这是一篇遵循 CC BY-NC-ND 许可证的开放获取文章 (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance with dense pixel-level annotations. However, drawing such labels on the giga-pixel whole slide images is extremely expensive and time-consuming. In this paper, we use only patch-level classification labels to achieve tissue semantic segmentation on histopathology images, finally reducing the annotation efforts. We propose a two-step model including a classification and a segmentation phases. In the classification phase, we propose a CAM-based model to generate pseudo masks by patch-level labels. In the segmentation phase, we achieve tissue semantic segmentation by our propose Multi-Layer Pseudo-Supervision. Several technical novelties have been proposed to reduce the information gap between pixel-level and patch-level annotations. As a part of this paper, we introduce a new weakly-supervised semantic segmentation (WSSS) dataset for lung adenocarcinoma (LUADHistoSeg). We conduct several experiments to evaluate our proposed model on two datasets. Our proposed model outperforms five state-of-the-art WSSS approaches. Note that we can achieve comparable quantitative and qualitative results with the fully-supervised model, with only around a 2% gap for MIoU and FwIoU. By comparing with manual labeling on a randomly sampled 100 patches dataset, patch-level labeling can greatly reduce the annotation time from hours to minutes. The source code and the released datasets are available at: https://github.com/ChuHan89/WSSS-Tissue . (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )