Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929657801080832 |
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| author | Nguyen, Manh Duong Nguyen, Dac Thai Nguyen, Trung Viet Yamada, Homi Pham, Huy Hieu Nguyen, Phi Le |
| author_facet | Nguyen, Manh Duong Nguyen, Dac Thai Nguyen, Trung Viet Yamada, Homi Pham, Huy Hieu Nguyen, Phi Le |
| contents | Osteosarcoma, the most common primary bone cancer, often requires accurate necrosis assessment from whole slide images (WSIs) for effective treatment planning and prognosis. However, manual assessments are subjective and prone to variability. In response, we introduce FDDM, a novel framework bridging the gap between patch classification and region-based segmentation. FDDM operates in two stages: patch-based classification, followed by region-based refinement, enabling cross-patch information intergation. Leveraging a newly curated dataset of osteosarcoma images, FDDM demonstrates superior segmentation performance, achieving up to a 10% improvement mIOU and a 32.12% enhancement in necrosis rate estimation over state-of-the-art methods. This framework sets a new benchmark in osteosarcoma assessment, highlighting the potential of foundation models and diffusion-based refinements in complex medical imaging tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01932 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models Nguyen, Manh Duong Nguyen, Dac Thai Nguyen, Trung Viet Yamada, Homi Pham, Huy Hieu Nguyen, Phi Le Computer Vision and Pattern Recognition Osteosarcoma, the most common primary bone cancer, often requires accurate necrosis assessment from whole slide images (WSIs) for effective treatment planning and prognosis. However, manual assessments are subjective and prone to variability. In response, we introduce FDDM, a novel framework bridging the gap between patch classification and region-based segmentation. FDDM operates in two stages: patch-based classification, followed by region-based refinement, enabling cross-patch information intergation. Leveraging a newly curated dataset of osteosarcoma images, FDDM demonstrates superior segmentation performance, achieving up to a 10% improvement mIOU and a 32.12% enhancement in necrosis rate estimation over state-of-the-art methods. This framework sets a new benchmark in osteosarcoma assessment, highlighting the potential of foundation models and diffusion-based refinements in complex medical imaging tasks. |
| title | Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.01932 |