ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 8 references that are in the map. First posted on arXiv 25 Feb 2022.
Builds on
5 of 8 references.
References · 5
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 387
- 2019Mish: A Self Regularized Non-Monotonic Activation Functioncited by 141
- 2021Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classificationcited by 89
- 2021CoNIC: Colon Nuclei Identification and Counting Challenge 2022cited by 44
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.