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.

使用U-Net多任务模型结合颜色和统计学增&测试时增强，来实现对组织分割和肿瘤检测的自动化，以期解决由于公共数据集稀缺和图像变异导致的困难。在CRC检测（SemiCOL）2023挑战上，该方法在被部分标注的数据集上取得了.8655的Dice分数（Arm 1）和.8515的Dice分数（Arm 2）用于组织分割，并在肿瘤检测方面分别达到了.9725（Arm 1）和.9750（Arm 2）的AUROC值。

结直肠癌组织学切片多任务学习的组织分割和肿瘤检测