A Multi-Instance Learning Approach for Improving Knee Osteoarthritis Diagnosis from MRI Data

Knee osteoarthritis (OA) is a prevalent and debilitating condition, significantly impacting quality of life and mobility. Traditional 3D image classification methods often falter in effectively diagnosing this complex condition, primarily due to their inability to capture and analyze the discrete, informative nuances inherent in individual MRI scan slices. To overcome this limitation, we present a pioneering deep learning framework leveraging Multi-Instance Learning (MIL) to enhance knee OA detection from 3D MRI scans. This novel approach treats each MRI slice as an independent instance, harnessing unique pathological details that contribute collectively to a more accurate and comprehensive diagnosis. Evaluated on an extensive dataset of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 0 0}$</tex> patients from the public OAI database, our MIL-based method demonstrates superior diagnostic performance, marking a significant leap over conventional imaging techniques, achieving an AUC of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 3. 4 1 \%}$</tex>.