Document Type

Conference Paper

Publication Date




Publication Title

Medical Imaging 2024: Image Processing, Proc. of SPIE 12926





Conference Name

Medical Imaging 2024: Image Processing, 19-22 February 2024, San Diego, California


One of the major neuropathological consequences of traumatic brain injury (TBI) is intracranial hemorrhage (ICH), which requires swift diagnosis to avert perilous outcomes. We present a new automatic hemorrhage segmentation technique via curriculum-based semi-supervised learning. It employs a pre-trained lightweight encoder-decoder framework (MobileNetV2) on labeled and unlabeled data. The model integrates consistency regularization for improved generalization, offering steady predictions from original and augmented versions of unlabeled data. The training procedure employs curriculum learning to progressively train the model at diverse complexity levels. We utilize the PhysioNet dataset to train and evaluate the proposed approach. The performance results surpass those of supervised model with an average Dice coefficient and Jaccard index of 0.573 and 0.428, respectively. Additionally, the method achieves 87.86% accuracy in hemorrhage classification and Cohen's Kappa value of 0.81, indicating substantial agreement with ground truth.


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Original Publication Citation

Emon, S. H., Tseng, T.-L., Pokojovy, M., McCaffrey, P., Moen, S., & Rahman, M. F. (2024). Automatic hemorrhage segmentation in brain CT scans using curriculum-based semi-supervised learning. In O. Colliot & J. Mitra (Eds.), Medical Imaging 2024: Image Processing, Proc. of SPIE 12926 (129262M). SPIE.


0000-0002-2122-2572 (Pokojovy)