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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.11722 |
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| _version_ | 1866918246740918272 |
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| author | Bai, Lin Li, Xiaoyang Huang, Liqiang Nguyen, Quynh Van Nguyen, Hien Prasad, Saurabh Maric, Dragan Redell, John Dash, Pramod Roysam, Badrinath |
| author_facet | Bai, Lin Li, Xiaoyang Huang, Liqiang Nguyen, Quynh Van Nguyen, Hien Prasad, Saurabh Maric, Dragan Redell, John Dash, Pramod Roysam, Badrinath |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 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 Bai, Lin Li, Xiaoyang Huang, Liqiang Nguyen, Quynh Van Nguyen, Hien Prasad, Saurabh Maric, Dragan Redell, John Dash, Pramod Roysam, Badrinath Computer Vision and Pattern Recognition 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. |
| title | 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 |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.11722 |