S2L-CM: Scribble-supervised nuclei segmentation in histopathology images using contrastive regularization and pixel-level multiple instance learning - Oh, Hyun-Jic; Min, Seonghui; Jeong, Won-Ki (Computers in biology and medicine, 2025)
Deep learning-based pathology nuclei segmentation algorithms have demonstrated remarkable performance. Conventional methods mostly focus on supervised learning, which requires significant manual effort to generate ground truth labels. Recently, weakly supervised learning has been extensively explored as a method for overcoming this limitation by training models with sparse annotations. However, the performance is inferior compared to that of fully supervised learning schemes. This paper proposes S2L-CM, a scribble-supervised nuclei segmentation framework based on two ideas; first, we leverage self-generated pseudo labels from user-given sparse scribble labels to train the deep learning model without full ground-truth labels, and second, we utilize multiscale contrastive regularization and pixel-level multiple-instance learning to further refine pseudo labels to improve segmentation performance. We demonstrate the effectiveness and robustness of our method on four nuclei datasets by comparing it with existing state-of-the-art methods. Code will be available at: https://github.com/hvcl/S2L-CM. [Display omitted] •Nuclei segmentation is vital for pathology image analysis but relies on labeled data.•Weakly supervised learning reduces data dependency while ensuring good performance.•Using model predictions as pseudo labels enhances model performance.•Multiscale contrastive regularization controls feature distances using prediction.•Pixel-level multiple instance learning contributes to reduce false predictions.