LoGo-MR uses neighbor-slice encoding and transformer-based multiple instance learning in three anatomical planes to predict 1-5 year breast cancer risk from MRI, achieving AUCs of 0.69-0.77 on a 7,500-patient cohort while providing interpretable risk maps.
2d, 2.5 d, or 3d? comparing dimen- sional approaches in deep neural networks for 3d medical image analysis.Journal of Imaging Informatics in Medicine, pages 1–23
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LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling
LoGo-MR uses neighbor-slice encoding and transformer-based multiple instance learning in three anatomical planes to predict 1-5 year breast cancer risk from MRI, achieving AUCs of 0.69-0.77 on a 7,500-patient cohort while providing interpretable risk maps.