Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917058900393984 |
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| author | Shakarami, Ashkan Farshad, Azade Yeganeh, Yousef Nicole, Lorenzo Schüffler, Peter Ghidoni, Stefano Navab, Nassir |
| author_facet | Shakarami, Ashkan Farshad, Azade Yeganeh, Yousef Nicole, Lorenzo Schüffler, Peter Ghidoni, Stefano Navab, Nassir |
| contents | We propose UTS, a unit-based tissue segmentation framework for histopathology that classifies each fixed-size 32 * 32 tile, rather than each pixel, as the segmentation unit. This approach reduces annotation effort and improves computational efficiency without compromising accuracy. To implement this approach, we introduce a Multi-Level Vision Transformer (L-ViT), which benefits the multi-level feature representation to capture both fine-grained morphology and global tissue context. Trained to segment breast tissue into three categories (infiltrating tumor, non-neoplastic stroma, and fat), UTS supports clinically relevant tasks such as tumor-stroma quantification and surgical margin assessment. Evaluated on 386,371 tiles from 459 H&E-stained regions, it outperforms U-Net variants and transformer-based baselines. Code and Dataset will be available at GitHub. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_12427 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation Shakarami, Ashkan Farshad, Azade Yeganeh, Yousef Nicole, Lorenzo Schüffler, Peter Ghidoni, Stefano Navab, Nassir Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning We propose UTS, a unit-based tissue segmentation framework for histopathology that classifies each fixed-size 32 * 32 tile, rather than each pixel, as the segmentation unit. This approach reduces annotation effort and improves computational efficiency without compromising accuracy. To implement this approach, we introduce a Multi-Level Vision Transformer (L-ViT), which benefits the multi-level feature representation to capture both fine-grained morphology and global tissue context. Trained to segment breast tissue into three categories (infiltrating tumor, non-neoplastic stroma, and fat), UTS supports clinically relevant tasks such as tumor-stroma quantification and surgical margin assessment. Evaluated on 386,371 tiles from 459 H&E-stained regions, it outperforms U-Net variants and transformer-based baselines. Code and Dataset will be available at GitHub. |
| title | Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2507.12427 |