@inproceedings{7281aedbd2f04082a68614a5c7ed78a1,
title = "Automatic Hemorrhage Segmentation in Brain CT Scans Using Curriculum-Based Semi-Supervised Learning",
abstract = "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.",
keywords = "Brain CT Scans, Curriculum Learning, Hemorrhage Segmentation, Intracranial Hemorrhage, Semi-Supervised Learning, Traumatic Brain Injury",
author = "Emon, {Solayman Hossain} and Tseng, {Tzu Liang} and Michael Pokojovy and Peter McCaffrey and Scott Moen and Rahman, {Md Fashiar}",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Medical Imaging 2024: Image Processing ; Conference date: 19-02-2024 Through 22-02-2024",
year = "2024",
doi = "10.1117/12.3006596",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Olivier Colliot and Jhimli Mitra",
booktitle = "Medical Imaging 2024",
}