Bridging 2D Efficiency and 3D Context: A Memory-Guided Framework for Knee MRI Multi-Label Classification
Year: 2026
Type: conference-paper
Abstract: Magnetic Resonance Imaging (MRI) plays a crucial role in detecting and classifying anatomical structures. However, automated analysis of medical magnetic resonance imaging remains challenging due to the many anatomical structures (e.g., the ACL and meniscus in the knee) and surrounding soft tissues that have very similar signal intensities, making their boundaries difficult to classify clearly. In this paper, we present a lightweight, end-to-end framework that bridges 2D and 3D analysis. By using a multi-scale center cropping strategy to capture anatomical details at differing resolutions and employing task-aware memory with depth-aware attention, our method automatically classifies the most diagnostic angle for each injury while maintaining high computational efficiency for clinical deployment. In evaluations on the MRNet dataset for the knee, our framework demonstrates excellent performance in labeling volumetric sequences, while also providing a mechanism to highlight the relevant anatomical features that contribute to diagnosis, thereby bridging the gap between automated scores and clinical reasoning. By combining multi-scale inputs with precise, context-aware feature encoding, our framework provides a robust, lightweight, and interpretable solution for diagnosing complex knee injuries.
Institution: Vietnamese-German University
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7202357653