Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing

• URL: http://arxiv.org/abs/2304.10671v2
• Date: Mon, 10 Jul 2023 21:59:38 GMT
• Title: Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing
• Authors: Ji Yu
• Abstract summary: This paper focuses on a weakly-supervised training setting for single-cell segmentation models. Of more interest is a proposed selflearning method called collaborative knowledge sharing. This strategy achieves selflearning by sharing knowledge between a principal model and a very lightweight collaborator model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite their superior performance, deep-learning methods often suffer from the disadvantage of needing large-scale well-annotated training data. In response, recent literature has seen a proliferation of efforts aimed at reducing the annotation burden. This paper focuses on a weakly-supervised training setting for single-cell segmentation models, where the only available training label is the rough locations of individual cells. The specific problem is of practical interest due to the widely available nuclei counter-stain data in biomedical literature, from which the cell locations can be derived programmatically. Of more general interest is a proposed self-learning method called collaborative knowledge sharing, which is related to but distinct from the more well-known consistency learning methods. This strategy achieves self-learning by sharing knowledge between a principal model and a very light-weight collaborator model. Importantly, the two models are entirely different in their architectures, capacities, and model outputs: In our case, the principal model approaches the segmentation problem from an object-detection perspective, whereas the collaborator model a sematic segmentation perspective. We assessed the effectiveness of this strategy by conducting experiments on LIVECell, a large single-cell segmentation dataset of bright-field images, and on A431 dataset, a fluorescence image dataset in which the location labels are generated automatically from nuclei counter-stain data. Implementing code is available at https://github.com/jiyuuchc/lacss

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