Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models

2025-01-03

AI Summary

Addressing the clinical challenges of low necrosis identification accuracy and poor cross-patch consistency in osteosarcoma whole-slide images (WSIs), this paper proposes FDDM, a two-stage framework: Stage I performs patch-level necrosis classification using a foundation model; Stage II refines predictions region-wise via a discrete diffusion model—marking the first integration of patch-level classification and region-level segmentation. We introduce the first publicly available osteosarcoma WSI necrosis annotation dataset. Experiments demonstrate that FDDM achieves a 10% improvement in mean Intersection-over-Union (mIoU) and reduces necrosis rate estimation error by 32.12%, significantly outperforming existing state-of-the-art methods. This work establishes a novel paradigm for precise pathological assessment and prognostic prediction in osteosarcoma.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Machine Learning: Other Foundations of Machine Learning
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• Osteosarcoma
• Necrosis Detection
• Treatment Prediction

Innovation

• FDDM
• Osteosarcoma Necrosis Detection
• Image Analysis Accuracy