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# Multi-view Learning

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  1. arXiv

    Two-view covariances no longer learned as one blob: CANE separates in-view and cross-view structure and cuts brain-age error to 4.97 years

    In two-view data, each view has its own covariance and a cross-view covariance links them, yet covariance neural networks handled only single-view data and could not distinguish these structures once the views were concatenated. CANE splits the model into an in-view covariance filtering stage and a cross-view covariance filtering stage, replacing the whitening and singular-vector selection of canonical correlation analysis with learnable polynomial responses on the covariance spectrum, and trains directly on the downstream task. Across seven brain-imaging and motion-sensing datasets, CANE is best or tied for best on six, reaching a mean absolute error of 4.97 years for age prediction on ADNI1, often with fewer parameters than a two-view VNN.

  2. Pediatric wrist fractures too subtle for one view: MV-CortexGuard pairs frontal and lateral evidence and lifts small-fracture AP by 6.6 points

    Subtle pediatric wrist fractures often appear on radiographs only as a focal cortical interruption or a fine linear lucency, and the epiphyses, growth plates and overlapping bones make these signs hard to separate. MV-CortexGuard compares cortical continuity across frontal and lateral views in a shared anatomical coordinate frame and refers high-risk examinations for expert review. On the internal GRAZPEDWRI-DX test set it reached 52.7% average precision on the size-defined small-fracture subset, 6.6 points above YOLOv10m, and 63.4% AP@50–95 overall.

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