Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting

• URL: http://arxiv.org/abs/2203.00262v1
• Date: Tue, 1 Mar 2022 06:48:23 GMT
• Title: Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting
• Authors: Chunhui Lin and Liukun Zhang and Lijian Mao and Min Wu and Dong Hu
• Abstract summary: We propose an approach that combine Separable-HoverNet and Instance-YOLOv5 to indentify colon and unbalanced nuclei. Our approach can achieve mPQ+ 0.389 on the nuclei and r2 0.599 on the Cellular Composition-Preliminary Test dataset.
• Score: 5.748087137894269
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (CPath). However, automatic recognition of different nuclei is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intraclass variability. In this work, we propose an approach that combine Separable-HoverNet and Instance-YOLOv5 to indentify colon nuclei small and unbalanced. Our approach can achieve mPQ+ 0.389 on the Segmentation and Classification-Preliminary Test Dataset and r2 0.599 on the Cellular Composition-Preliminary Test Dataset on ISBI 2022 CoNIC Challenge.

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