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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.12401 |
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| _version_ | 1866912277227110400 |
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| author | Zhao, Jianwei Li, Xin Yang, Fan Zhai, Qiang Luo, Ao Zhao, Yang Cheng, Hong Fu, Huazhu |
| author_facet | Zhao, Jianwei Li, Xin Yang, Fan Zhai, Qiang Luo, Ao Zhao, Yang Cheng, Hong Fu, Huazhu |
| contents | Whole Slide Image (WSI) classification poses unique challenges due to the vast image size and numerous non-informative regions, which introduce noise and cause data imbalance during feature aggregation. To address these issues, we propose MExD, an Expert-Infused Diffusion Model that combines the strengths of a Mixture-of-Experts (MoE) mechanism with a diffusion model for enhanced classification. MExD balances patch feature distribution through a novel MoE-based aggregator that selectively emphasizes relevant information, effectively filtering noise, addressing data imbalance, and extracting essential features. These features are then integrated via a diffusion-based generative process to directly yield the class distribution for the WSI. Moving beyond conventional discriminative approaches, MExD represents the first generative strategy in WSI classification, capturing fine-grained details for robust and precise results. Our MExD is validated on three widely-used benchmarks-Camelyon16, TCGA-NSCLC, and BRACS consistently achieving state-of-the-art performance in both binary and multi-class tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12401 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification Zhao, Jianwei Li, Xin Yang, Fan Zhai, Qiang Luo, Ao Zhao, Yang Cheng, Hong Fu, Huazhu Computer Vision and Pattern Recognition Whole Slide Image (WSI) classification poses unique challenges due to the vast image size and numerous non-informative regions, which introduce noise and cause data imbalance during feature aggregation. To address these issues, we propose MExD, an Expert-Infused Diffusion Model that combines the strengths of a Mixture-of-Experts (MoE) mechanism with a diffusion model for enhanced classification. MExD balances patch feature distribution through a novel MoE-based aggregator that selectively emphasizes relevant information, effectively filtering noise, addressing data imbalance, and extracting essential features. These features are then integrated via a diffusion-based generative process to directly yield the class distribution for the WSI. Moving beyond conventional discriminative approaches, MExD represents the first generative strategy in WSI classification, capturing fine-grained details for robust and precise results. Our MExD is validated on three widely-used benchmarks-Camelyon16, TCGA-NSCLC, and BRACS consistently achieving state-of-the-art performance in both binary and multi-class tasks. |
| title | MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.12401 |