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Water security assessment framework for deltas of the transboundary river basins
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Quantitative Trait Loci Analysis of Folate Content in Dry Beans, Phaseolus vulgaris L.
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Experiences and overall impact reported by people living with obesity: results from a multinational study
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Solo Sonographically Guided PCNL under Spinal Anesthesia: Defining Predictors of Success
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A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer
Published 2025-02-01“…This study aims to develop and validate a transformer-based deep learning (DL) model based on pelvic MR images, integrated with clinical features, to predict RCLM.MethodsA total of 819 patients with histologically confirmed rectal cancer who underwent preoperative pelvis MRI and carcinoembryonic antigen (CEA) tests were enrolled. Six state-of-the-art DL methods (Resnet18, EfficientNetb0, MobileNet, ShuffleNet, DenseNet, and our transformer-based model) were trained and tested on T2WI and DWI to predict RCLM. …”
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