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A Machine Learning Approach to Identify Previously Unconsidered Causes for Complications in Aesthetic Breast Augmentation
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Feasibility of anomaly score detected with deep learning in irradiated breast cancer patients with reconstruction
Validating machine learning approaches for prediction of donor related complication in microsurgical breast reconstruction: a retrospective cohort study
Validating machine learning approaches for prediction of donor related complication in microsurgical breast reconstruction: a retrospective cohort study
Validating machine learning approaches for prediction of donor related complication in microsurgical breast reconstruction: a retrospective cohort study
Validating machine learning approaches for prediction of donor related complication in microsurgical breast reconstruction: a retrospective cohort study
PDF) A Retrospective Analysis of 3,000 Primary Aesthetic Breast Augmentations: Postoperative Complications and Associated Factors
A Machine Learning Approach to Identify Previously Unconsidered Causes for Complications in Aesthetic Breast Augmentation
Patient Experience Library
Björn BEHR, Ruhr-Universität Bochum, Bochum, RUB, Department of Plastic Surgery
PDF) A breast prosthesis infection update: Two-year incidence, risk factors and management at single institution
Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape - Critical Care Clinics
Volume 20 Issue 2 by Western Journal of Emergency Medicine - Issuu
Anomaly Detection of Breast Cancer Using Deep Learning
