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 -ofdistribution 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 U.S. institution from 2010 to 2017 and used to generate pseudo labels on a separate unlabeled corpus of 25 000 examinations from the Radiological Society of North America and American Society of Neuroradiology. A second "student" model was trained on this combined pixel- and pseudo -labeled dataset. Hyperparameter tuning was performed on a validation set of 93 scans. Testing for both classification ( n = 481 examinations) and segmentation ( n = 23 examinations, or 529 images) was performed on CQ500, a dataset of 481 scans performed in India, to evaluate out -of -distribution generalizability. The semisupervised model was compared with a baseline model trained on only labeled data using area under the receiver operating characteristic curve, Dice similarity coefficient, and average precision metrics. Results: The semi -supervised model achieved a statistically significant higher examination area under the receiver operating characteristic curve on CQ500 compared with the baseline (0.939 [95% CI: 0.938, 0.940] vs 0.907 [95% CI: 0.906, 0.908]; P = .009). It also achieved a higher Dice similarity coefficient (0.829 [95% CI: 0.825, 0.833] vs 0.809 [95% CI: 0.803, 0.812]; P = .012) and pixel average precision (0.848 [95% CI: 0.843, 0.853]) vs 0.828 [95% CI: 0.817, 0.828]) compared with the baseline. Conclusion: The addition of unlabeled data in a semi -supervised learning framework demonstrates stronger generalizability potential for intracranial hemorrhage detection and segmentation compared with a supervised baseline.