@inproceedings{92514f986ddf4bd28804d11573b988fd,
title = "Efficient Cardiac Image Segmentation with Compressed Vision Transformers and Post-training Quantization",
abstract = "The high prevalence of cardiovascular diseases (CVDs) worldwide requires accurate diagnostic imaging, particularly through magnetic resonance imaging (MRI). The framework includes preprocessing for region-of-interest segmentation via ViTs, followed by PTQ to reduce model size while maintaining segmentation accuracy. Using a small calibration dataset, we apply PTQ to compress the ViT, significantly reducing storage requirements and latency without compromising precision. Experimental results indicate that Float16 quantization achieves an optimal balance between compression rate and segmentation accuracy, demonstrating the feasibility of ViTs for real-time applications.",
keywords = "Cardiac image segmentation, Deep model compression, Post-training quantization, Vision transformer",
author = "Assia Boukhamla and Lafia, \{Tamer Abderrahmane\} and Nabiha Azizi and Belhaouari, \{Samir Brahim\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.",
year = "2025",
month = jul,
day = "20",
doi = "10.1007/978-981-96-6103-9\_5",
language = "English",
isbn = "978-981-96-6105-3",
volume = "40",
series = "Lecture Notes In Computational Vision And Biomechanics",
publisher = "Springer Science and Business Media B.V.",
pages = "55--69",
editor = "RS Sherratt and JMRS Tavares and S Fong and N Dey",
booktitle = "Biotechnology And Health Sciences, Biocom 2025",
address = "Germany",
}