Semi-supervised learning for generalizable intracranial hemorrhage detection and segmentation

Purpose: To develop and evaluate a semi-supervised learning model for intracranial hemorrhage detection and segmentation on an out-of-distribution head CT evaluation set.
Materials and Methods: This retrospective study used semi-supervised learning to bootstrap performance. An initial "teacher" deep learning model was trained on 457 pixel-labeled head CT scans collected from one US institution from 2010-2017 and used to generate pseudo-labels on a separate unlabeled corpus of 25000 examinations from the RSNA and ASNR. A second "student" model was trained on …

bootstrap cs.cv cs.lg deep learning detection distribution eess.iv evaluation head materials performance pixel retrospective scans segmentation semi-supervised semi-supervised learning set study supervised learning