  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 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 semi-supervised model was compared
with a baseline model trained on only labeled data using area under the
receiver operating characteristic curve (AUC), Dice similarity coefficient
(DSC), and average precision (AP) metrics.
  Results: The semi-supervised model achieved statistically significantly
higher examination AUC on CQ500 compared with the baseline (0.939 [0.938,
0.940] vs. 0.907 [0.906, 0.908]) (p=0.009). It also achieved a higher DSC
(0.829 [0.825, 0.833] vs. 0.809 [0.803, 0.812]) (p=0.012) and Pixel AP (0.848
[0.843, 0.853]) vs. 0.828 [0.817, 0.828]) compared to 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.
