3D-HSPA: Integrating 3D Spatial Information with Hierarchical Slice-Patch Attention for Knee MRI Analysis

Résumé fourni par la source

Magnetic Resonance Imaging (MRI) is a crucial modality for diagnosing knee joint diseases. However, accurately extracting disease-relevant features from complex multi-slice, multi-sequence MRI scans remains a considerable challenge. To address this, we propose 3D-HSPA, a novel diagnostic framework for multi-slice, multi-sequence knee MRI, which integrates disease-specific information at both slice and patch levels and establishes intrinsic spatial connections among different sequences. Specifically, we introduce a patch-level and slice-level label attention mechanism, guiding the model to automatically learn a precise alignment between image regions and disease labels. Furthermore, by mapping 2D images from various sequences into a unified 3D spatial coordinate system, we enhance the spatial consistency and robustness of the attention distributions. We validated 3D-HSPA on a large-scale MRI dataset comprising 50 fine-grained types of knee joint diseases. The experimental results demonstrate that 3D-HSPA not only achieves superior diagnostic performance but also exhibits strong model interpretability.

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