Relieving Pixel-Wise Labeling Effort for Pathology Image Segmentation with Self-Training
Abstract
Data scarcity is a common issue when training deep learning models for digital pathology, as large exhaustively-annotated image datasets are difficult to obtain. In this paper, we propose a self-training based approach that can exploit both (few) exhaustively annotated images and (very) sparsely-annotated images to improve the training of deep learning models for image segmentation tasks. The approach is evaluated on three public and one in-house dataset, representing a diverse set of segmentation tasks in digital pathology. The experimental results show that self-training allows to bring significant model improvement by incorporating sparsely annotated images and proves to be a good strategy to relieve labeling effort in the digital pathology domain.
Cite
Text
Mormont et al. "Relieving Pixel-Wise Labeling Effort for Pathology Image Segmentation with Self-Training." European Conference on Computer Vision Workshops, 2022. doi:10.1007/978-3-031-25082-8_39Markdown
[Mormont et al. "Relieving Pixel-Wise Labeling Effort for Pathology Image Segmentation with Self-Training." European Conference on Computer Vision Workshops, 2022.](https://mlanthology.org/eccvw/2022/mormont2022eccvw-relieving/) doi:10.1007/978-3-031-25082-8_39BibTeX
@inproceedings{mormont2022eccvw-relieving,
title = {{Relieving Pixel-Wise Labeling Effort for Pathology Image Segmentation with Self-Training}},
author = {Mormont, Romain and Testouri, Mehdi and Marée, Raphaël and Geurts, Pierre},
booktitle = {European Conference on Computer Vision Workshops},
year = {2022},
pages = {577-592},
doi = {10.1007/978-3-031-25082-8_39},
url = {https://mlanthology.org/eccvw/2022/mormont2022eccvw-relieving/}
}