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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.23799 |
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| _version_ | 1866915959522983936 |
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| author | Shokrollahi, Yasin Gonzales, Karina B. Pinao Toro, Elizve N. Barrientos Acosta, Paul Team, Patient Mosaic Chen, Pingjun Yuan, Yinyin Pan, Xiaoxi |
| author_facet | Shokrollahi, Yasin Gonzales, Karina B. Pinao Toro, Elizve N. Barrientos Acosta, Paul Team, Patient Mosaic Chen, Pingjun Yuan, Yinyin Pan, Xiaoxi |
| contents | Accurate whole-cell and nuclear segmentation is essential for precision pathology and spatial omics, yet routine hematoxylin and eosin (H&E) staining provides limited cytoplasmic contrast, restricting analyses to nuclei. Multiplex immunofluorescence (mIF) facilitates precise whole-cell delineation but remains constrained by cost and accessibility. We introduce VitaminP, a cross-modal learning framework enabling whole cell segmentation from H&E images. By learning from paired H&E-mIF data, VitaminP transfers molecular boundary information from mIF to overcome cytoplasmic contrast in H&E, establishing cross-modal supervision as a general strategy for recovering missing biological structure. We train VitaminP on 14 public datasets covering 34 cancer types and over 7 million instances, integrating publicly available labels with extensive annotations generated in this study, forming one of the largest resources for segmentation. VitaminP outperforms four state-of-the-art methods and generalizes to unseen datasets, including an in-house dataset spanning 24 rare cancer types. We further developed VitaminPScope, an open-source platform providing an interface for scalable inference and enabling broad adoption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23799 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VitaminP: cross-modal learning enables whole-cell segmentation from routine histology Shokrollahi, Yasin Gonzales, Karina B. Pinao Toro, Elizve N. Barrientos Acosta, Paul Team, Patient Mosaic Chen, Pingjun Yuan, Yinyin Pan, Xiaoxi Computer Vision and Pattern Recognition Accurate whole-cell and nuclear segmentation is essential for precision pathology and spatial omics, yet routine hematoxylin and eosin (H&E) staining provides limited cytoplasmic contrast, restricting analyses to nuclei. Multiplex immunofluorescence (mIF) facilitates precise whole-cell delineation but remains constrained by cost and accessibility. We introduce VitaminP, a cross-modal learning framework enabling whole cell segmentation from H&E images. By learning from paired H&E-mIF data, VitaminP transfers molecular boundary information from mIF to overcome cytoplasmic contrast in H&E, establishing cross-modal supervision as a general strategy for recovering missing biological structure. We train VitaminP on 14 public datasets covering 34 cancer types and over 7 million instances, integrating publicly available labels with extensive annotations generated in this study, forming one of the largest resources for segmentation. VitaminP outperforms four state-of-the-art methods and generalizes to unseen datasets, including an in-house dataset spanning 24 rare cancer types. We further developed VitaminPScope, an open-source platform providing an interface for scalable inference and enabling broad adoption. |
| title | VitaminP: cross-modal learning enables whole-cell segmentation from routine histology |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.23799 |