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Abstract: How to Generate Patient Benefit with Surgical Data Science - Results of an International Delphi Process., , , and . Bildverarbeitung für die Medizin, page 113. Springer, (2022)Understanding metric-related pitfalls in image analysis validation., , , , , , , , , and 68 other author(s). CoRR, (2023)Why is the Winner the Best?, , , , , , , , , and 115 other author(s). CVPR, page 19955-19966. IEEE, (2023)A Delphi consensus statement for digital surgery., , , , , , , , , and 30 other author(s). npj Digit. Medicine, (2022)BIAS: Transparent reporting of biomedical image analysis challenges., , , , , , , , , and 4 other author(s). Medical Image Anal., (2020)Metrics reloaded: Pitfalls and recommendations for image analysis validation., , , , , , , , , and 57 other author(s). CoRR, (2022)Why is the winner the best?, , , , , , , , , and 41 other author(s). CoRR, (2023)Common Limitations of Image Processing Metrics: A Picture Story., , , , , , , , , and 24 other author(s). CoRR, (2021)Surgical Data Science - from Concepts to Clinical Translation., , , , , , , , , and 26 other author(s). CoRR, (2020)Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room., , , , , , , , , and 20 other author(s). CoRR, (2020)