Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 8 references that are in the map. First posted on arXiv 1 Mar 2022.
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References · 8
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2017Mask R-CNNcited by 7,277
- 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
- 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 388
- 2019SOLO: Segmenting Objects by Locationscited by 289
- 2021Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classificationcited by 89
- 2022Equalized Focal Loss for Dense Long-Tailed Object Detectioncited by 46
- 2021CoNIC: Colon Nuclei Identification and Counting Challenge 2022cited by 45
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