Weak-to-Strong Generalization Enables Fully Automated De Novo Training of Multi-head Mask-RCNN Model for Segmenting Densely Overlapping Cell Nuclei in Multiplex Whole-slice Brain Images

Lin Bai; Xiaoyang Li; Liqiang Huang; Quynh Nguyen; Hien Van Nguyen; Saurabh Prasad; Dragan Maric; John Redell; Pramod Dash; Badrinath Roysam

arXiv · 2512.11722

Weak-to-Strong Generalization Enables Fully Automated De Novo Training of Multi-head Mask-RCNN Model for Segmenting Densely Overlapping Cell Nuclei in Multiplex Whole-slice Brain Images

Lin Bai·Xiaoyang Li·Liqiang Huang·Quynh Nguyen·Hien Van Nguyen·Saurabh Prasad·Dragan Maric·John Redell·Pramod Dash·Badrinath Roysam

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Abstract

We present a weak to strong generalization methodology for fully automated training of a multi-head extension of the Mask-RCNN method with efficient channel attention for reliable segmentation of overlapping cell nuclei in multiplex cyclic immunofluorescent (IF) whole-slide images (WSI), and present evidence for pseudo-label correction and coverage expansion, the key phenomena underlying weak to strong generalization. This method can learn to segment de novo a new class of images from a new instrument and/or a new imaging protocol without the need for human annotations. We also present metrics for automated self-diagnosis of segmentation quality in production environments, where human visual proofreading of massive WSI images is unaffordable. Our method was benchmarked against five current widely used methods and showed a significant improvement. The code, sample WSI images, and high-resolution segmentation results are provided in open form for community adoption and adaptation.

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BibTeX RIS

Lin Bai, Xiaoyang Li, Liqiang Huang, Quynh Nguyen, Hien Van Nguyen, Saurabh Prasad, Dragan Maric, John Redell, Pramod Dash, Badrinath Roysam. 2025-12-12. Weak-to-Strong Generalization Enables Fully Automated De Novo Training of Multi-head Mask-RCNN Model for Segmenting Densely Overlapping Cell Nuclei in Multiplex Whole-slice Brain Images. https://arxiv.org/abs/2512.11722

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