ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network
This work addresses a domain-specific problem for pathologists, but appears incremental as it builds on existing challenge frameworks.
The paper tackles automated cell nucleus segmentation and classification for histopathological images, reporting preliminary evaluation results for their method ciscNet on the CoNIC Challenge 2022 dataset.
Automated cell nucleus segmentation and classification are required to assist pathologists in their decision making. The Colon Nuclei Identification and Counting Challenge 2022 (CoNIC Challenge 2022) supports the development and comparability of segmentation and classification methods for histopathological images. In this contribution, we describe our CoNIC Challenge 2022 method ciscNet to segment, classify and count cell nuclei, and report preliminary evaluation results. Our code is available at https://git.scc.kit.edu/ciscnet/ciscnet-conic-2022.