Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models
This work addresses the need for accurate and automated assessment of osteosarcoma necrosis in medical imaging, which is crucial for treatment planning and prognosis, representing a domain-specific advancement.
The paper tackles the problem of subjective and variable manual necrosis assessment in osteosarcoma from whole slide images by introducing FDDM, a framework that bridges patch classification and region-based segmentation, achieving up to a 10% improvement in mIOU and a 32.12% enhancement in necrosis rate estimation over state-of-the-art methods.
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.