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- W4295915700 abstract "AbstractMagnetic resonance imaging (MRI) has become necessary in clinical diagnosis for knee osteoarthritis (OA), while deep neural networks can contribute to the computer-assisted diagnosis. Recent works prove that instead of only using a single-view MR image (e.g., sagittal), integrating multi-view MR images can boost the performance of the deep network. However, existing multi-view networks typically encode each MRI view to a feature vector, fuse the feature vectors of all views, and then derive the final output using a set of shallow computations. Such a global fusion scheme happens at a coarse granularity, which may not effectively localize the often tiny abnormality related to the onset of OA. Therefore, this paper proposes a Local Graph Fusion Network (LGF-Net), which implements graph-based representation of knee MR images and multi-view fusion for OA diagnosis. We first model the multi-view MR images to a unified knee graph. Then, the patches of the same location yet from different views are encoded to one-dimensional features and are exchanged mutually during fusing. The local fusion of the features further propagates following edges by Graph Transformer Network in the LGF-Net, which finally yields the grade of OA. The experimental results show that the proposed framework outperforms state-of-the-art methods, demonstrating the effectiveness of local graph fusion in OA diagnosis.KeywordsKnee OA diagnosisGraph representationMulti-view MRI" @default.
- W4295915700 created "2022-09-16" @default.
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- W4295915700 date "2022-01-01" @default.
- W4295915700 modified "2023-10-15" @default.
- W4295915700 title "Local Graph Fusion of Multi-view MR Images for Knee Osteoarthritis Diagnosis" @default.
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- W4295915700 doi "https://doi.org/10.1007/978-3-031-16437-8_53" @default.
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