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_jax

翻译：尽管深度学习方法性能优越，但其通常面临需要大规模高质量标注训练数据的缺陷。为此，近年来大量研究致力于降低标注负担。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程序化推导出细胞位置，该特定问题具有实际应用价值。更普遍而言，我们提出了一种名为"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：在本研究中，主模型采用目标检测视角处理分割问题，而协作模型则从语义分割视角切入。我们通过在LIVECell（大型明场图像单细胞分割数据集）与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）上开展实验验证了该策略的有效性。实现代码见https://github.com/jiyuuchc/lacss_jax