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

翻译：尽管深度学习方法性能优越，但通常需要大规模高质量标注训练数据的缺点。为此，近年涌现大量旨在减轻标注负担的研究工作。本文聚焦于单细胞分割模型的弱监督训练场景，其中唯一可用的训练标签是单个细胞的粗略位置。由于生物医学文献中广泛存在的细胞核复染数据可通过编程方式导出细胞位置，该具体问题具有实际应用价值。更具一般意义的是我们提出的称为协作知识共享的自学习方法，该方法与更广为人知的一致性学习方法相关但存在本质区别。该策略通过在主模型与极轻量级协作模型之间共享知识实现自学习。关键在于这两个模型在架构、容量及输出形式上完全不同：在我们的案例中，主模型从目标检测视角处理分割问题，而协作模型则采用语义分割视角。我们通过在LIVECell（大型明场图像单细胞分割数据集）和A431数据集（利用细胞核复染数据自动生成位置标签的荧光图像数据集）上的实验验证了该策略的有效性。实现代码见 https://github.com/jiyuuchc/lacss