ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network

2022/02/25 by Moritz Böhland, Oliver Neumann, Böhland, Moritz +11
Computer Science · Medicine · #AI in cancer detection #Digital Imaging for Blood Diseases #Colorectal Cancer Screening and Detection

paper · pdf · doi:10.48550/arxiv.2202.13960

Abstract

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.

Related

• CoNIC: Colon Nuclei Identification and Counting Challenge 2022
• Nuclei panoptic segmentation and composition regression with multi-task deep neural networks
• A Standardized Pipeline for Colon Nuclei Identification and Counting Challenge
• CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
• Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting