MedCAM-OsteoCls: Medical Context Aware Multimodal Classification of Knee Osteoarthritis

Code

• github.com/adaydar/MedCAM-OsteoCls
  In paperpytorch★ 0

Abstract

Knee Osteoarthritis (KOA) is a degenerative musculoskeletal joint disorder that significantly impacts middle-aged and elderly individuals. Although X-rays and MRIs are clinically used to identify such disorders, combining these imaging modalities is challenging due to the distinct nature of the data, as MRI volume provides detailed views of cartilage and soft tissues, while X-rays offer a global perspective of bone anatomy and positioning in single scan. In this context, the proposed work aims to address two key challenges: (1) how to automatically select the most informative slices from large and dis-organized MRI volume in a clinically relevant way and (2) how to effectively capture the discriminative multimodal interactions between X-ray and MRI modalities. In order to solve these issues, the Context-Guided Slice Selection and Prioritization (CG-SSP) module and Xray-MRI Cross-Attention (XMRCA) module are proposed. The CG-SSP module focuses on rejecting non-efficient slices and prioritizing the efficient slices in a multi-stage approach, first by segmenting the Femoral Cartilage (FC) then extracting the sequential patches of FC located in each MRI slice followed by assigning the attention weight to each of these slices based on learned structural complexity, while the XMRCA module focuses on learning the global interactions between X-ray and MRI feature space in a computationally efficient manner. Overall, the proposed model achieved an accuracy improvement of 2.66% over the state-of-the-art in unimodal and multimodal baselines on the OAI dataset. The proposed model identifies key diagnostic slices from MRI and can provide valuable insights into interactions between X-ray and MRI data for radiological KOA diagnosis in an end-to-end manner. Code will be made available at: https://github.com/adaydar/MedCAM-OsteoCls

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Reproductions