Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models
Journal:
arXiv
Published Date:
Jan 3, 2025
Abstract
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.
