Multi-task learning for tissue segmentation and tumor detection in colorectal cancer histology slides

• URL: http://arxiv.org/abs/2304.03101v1
• Date: Thu, 6 Apr 2023 14:26:41 GMT
• Title: Multi-task learning for tissue segmentation and tumor detection in colorectal cancer histology slides
• Authors: Lydia A. Schoenpflug, Maxime W. Lafarge, Anja L. Frei, Viktor H. Koelzer
• Abstract summary: We propose a U-Net based multi-task model combined with channel-wise and image-statistics-based color augmentations. 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.
• Score: 0.9176056742068814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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