Nuclei panoptic segmentation and composition regression with multi-task deep neural networks
Year: 2022
Type: preprint
Abstract: Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology. The Colon Nuclei Identification and Counting (CoNIC) Challenge is held to help drive forward research and innovation for automatic nuclei recognition in computational pathology. This report describes our proposed method submitted to the CoNIC challenge. Our method employs a multi-task learning framework, which performs a panoptic segmentation task and a regression task. For the panoptic segmentation task, we use encoder-decoder type deep neural networks predicting a direction map in addition to a segmentation map in order to separate neighboring nuclei into different instances
Source: arXiv (Cornell University)
Authors: Kondo, Satoshi, Kasai, Satoshi
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4226322332