Hierarchical-Instance Contrastive Learning for Minority Detection on Imbalanced Medical Datasets.

Deep learning methods are often hampered by issues such as data imbalance and data-hungry. In medical imaging, malignant or rare diseases are frequently of minority classes in the dataset, featured by diversified distribution. Besides that, insufficient labels and unseen cases also present conundrums for training on the minority classes.

Development and Evaluation of a Deep Learning Algorithm for Rib Segmentation and Fracture Detection from Multicenter Chest CT Images.

Purpose: To evaluate the performance of a deep learning-based algorithm for automatic detection and labeling of rib fractures from multicenter chest CT images.

Materials And Methods: This retrospective study included 10 943 patients (mean age, 55 years; 6418 men) from six hospitals (January 1, 2017 to December 30, 2019), which consisted of patients with and without rib fractures who underwent CT. The patients were separated into one training set ( = 2425), two lesion-level test sets ( = 362 and 105), and one examination-level test set ( = 8051).