HoVer-UNet: Accelerating HoVerNet with UNet-based multi-class nuclei segmentation via knowledge distillation
Abstract: We present HoVer-UNet, an approach to distill the knowledge of the multi-branch HoVerNet framework for nuclei instance segmentation and classification in histopathology. We propose a compact, streamlined single UNet network with a Mix Vision Transformer backbone, and equip it with a custom loss function to optimally encode the distilled knowledge of HoVerNet, reducing computational requirements without compromising performances. We show that our model achieved results comparable to HoVerNet on the public PanNuke and Consep datasets with a three-fold reduction in inference time. We make the code of our model publicly available at https://github.com/DIAGNijmegen/HoVer-UNet.
- “Panoptic segmentation. in 2019 ieee,” in CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 9396–9405.
- “Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study,” Gut, 2023.
- “Computational nuclei segmentation methods in digital pathology: a survey,” Archives of Computational Methods in Engineering, vol. 28, pp. 1–13, 2021.
- “Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images,” Medical Image Analysis, vol. 58, pp. 101563, 2019.
- “Histocartography: A toolkit for graph analytics in digital pathology,” in MICCAI Workshop on Computational Pathology. PMLR, 2021, pp. 117–128.
- “Pan-cancer integrative histology-genomic analysis via multimodal deep learning,” Cancer Cell, vol. 40, no. 8, pp. 865–878, 2022.
- “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531, 2015.
- “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, 2015, pp. 234–241.
- “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in Neural Information Processing Systems, vol. 34, pp. 12077–12090, 2021.
- “Pannuke dataset extension, insights and baselines,” arXiv preprint arXiv:2003.10778, 2020.
- “Segmentation of nuclei in histopathology images by deep regression of the distance map,” IEEE transactions on medical imaging, vol. 38, no. 2, pp. 448–459, 2018.
- “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2961–2969.
- “Micro-net: A unified model for segmentation of various objects in microscopy images,” Medical image analysis, vol. 52, pp. 160–173, 2019.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
