Automating tissue segmentation and tumor detection in histopathology images of colorectal cancer (CRC) is an enabler for faster diagnostic pathology workflows. At the same time it is a challenging task due to low availability of public annotated datasets and high variability of image appearance. The semi-supervised learning for CRC detection (SemiCOL) challenge 2023 provides partially annotated data to encourage the development of automated solutions for tissue segmentation and tumor detection. We propose a U-Net based multi-task model combined with channel-wise and image-statistics-based color augmentations, as well as test-time augmentation, as a candidate solution to the SemiCOL challenge. Our approach achieved a multi-task Dice score of .8655 (Arm 1) and .8515 (Arm 2) for tissue segmentation and AUROC of .9725 (Arm 1) and 0.9750 (Arm 2) for tumor detection on the challenge validation set. The source code for our approach is made publicly available at https://github.com/lely475/CTPLab_SemiCOL2023.

翻译：自动化结直肠癌组织病理学图像中的组织分割与肿瘤检测，可加速诊断病理工作流程。然而，由于公共标注数据集稀缺且图像外观高度变异，该任务极具挑战性。2023年半监督结直肠癌检测挑战赛（SemiCOL）提供了部分标注数据，旨在推动组织分割与肿瘤检测自动化解决方案的开发。本文提出一种基于U-Net的多任务模型，结合通道级与基于图像统计的颜色增强策略及测试时增强技术，作为SemiCOL挑战的候选解决方案。在挑战赛验证集上，我们的方法在多任务组织分割中取得了0.8655（Arm 1）和0.8515（Arm 2）的Dice系数，在肿瘤检测中取得了0.9725（Arm 1）和0.9750（Arm 2）的AUROC值。源代码已公开于https://github.com/lely475/CTPLab_SemiCOL2023。