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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/2511.03751 |
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| _version_ | 1866912689809260544 |
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| author | Fathollahian, Hossein Zhao, Siyuan Nipu, Nafiul Marai, G. Elisabeta |
| author_facet | Fathollahian, Hossein Zhao, Siyuan Nipu, Nafiul Marai, G. Elisabeta |
| contents | High-dimensional tissue imaging generates highly complex 3D data containing multiple biomarkers, making it challenging to identify biologically relevant regions without an expert user specifying manual labels for regions of interest. We introduce an approach to automatically identifying regions of interest (ROIs) in the 3D microscopy data. Our approach is based on a novel self-supervised multi-layer graph attention network (SSGAT), coupled with a React interactive interface wrapped around Vitessce. SSGAT employs an adversarial self-supervised learning objective to identify meaningful immune microenvironments through marker interactions. Our method reveals complex spatial bioreactions that can be visually assessed to assess their distribution across tissue. Index Terms: Biomedical visualization, graph attention networks,self-supervised learning, spatial interaction analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03751 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Attention-based ROI Discovery in 3D Tissue Images Fathollahian, Hossein Zhao, Siyuan Nipu, Nafiul Marai, G. Elisabeta Quantitative Methods High-dimensional tissue imaging generates highly complex 3D data containing multiple biomarkers, making it challenging to identify biologically relevant regions without an expert user specifying manual labels for regions of interest. We introduce an approach to automatically identifying regions of interest (ROIs) in the 3D microscopy data. Our approach is based on a novel self-supervised multi-layer graph attention network (SSGAT), coupled with a React interactive interface wrapped around Vitessce. SSGAT employs an adversarial self-supervised learning objective to identify meaningful immune microenvironments through marker interactions. Our method reveals complex spatial bioreactions that can be visually assessed to assess their distribution across tissue. Index Terms: Biomedical visualization, graph attention networks,self-supervised learning, spatial interaction analysis. |
| title | Attention-based ROI Discovery in 3D Tissue Images |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2511.03751 |