S2L-CM: Scribble-supervised nuclei segmentation in histopathology images using contrastive regularization and pixel-level multiple instance learning

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 explore...

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Vydané v:	Computers in biology and medicine Ročník 192; číslo Pt B; s. 110293
Hlavní autori:	Oh, Hyun-Jic, Min, Seonghui, Jeong, Won-Ki
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Ltd 01.06.2025
Predmet:	Algorithms Cell Nucleus - pathology Deep Learning Humans Image Processing, Computer-Assisted - methods Internal Medicine Multiple-Instance Learning Algorithms Multiscale contrastive regularization Other Pixel-level multiple instance learning Pseudo label supervision Weakly supervised nuclei segmentation Pixel-level multiple instance learning Weakly supervised nuclei segmentation Pseudo label supervision Multiscale contrastive regularization
ISSN:	0010-4825, 1879-0534, 1879-0534
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Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0010-4825 1879-0534 1879-0534
DOI:	10.1016/j.compbiomed.2025.110293