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ORIGINAL RESEARCH ARTICLE

Optimizing knowledge distillation for efficient breast ultrasound image segmentation: Insights and performance enhancement

Bahareh Behboodi1* ,  Rupert Brooks2 ,  Hassan Rivaz1,3
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1 Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada
2 Microsoft Canada, Montreal, Quebec, Canada
3 School of Health, Concordia University, Montreal, Quebec, Canada
AIH, 3509 https://doi.org/10.36922/aih.3509
Submitted: 26 April 2024 | Accepted: 4 September 2024 | Published: 20 December 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Most modern models designed for ultrasound (US) image segmentation are characterized by high computational and memory requirements, limiting their practical utility in point-of-care US settings. Consequently, researchers have devised innovative approaches to compress these large models, enabling the training of smaller networks capable of achieving comparable generalization performance. Among these strategies, knowledge distillation (KD) has emerged as particularly suitable for scenarios involving small datasets or where significant efficiency improvements are desired. While previous KD-based methods have focused on extracting comprehensive information from diverse levels of teacher representation, they often overlook the identification of the most effective representation level. Additionally, many existing techniques propose intricate strategies that present implementation challenges. To address this gap, our study concentrates on selecting optimal teacher representations from various levels. Through an exhaustive analysis of KD pathways, loss functions, and the impact of augmentation, we offer valuable insights into the mechanisms underlying knowledge transfer from the teacher to the student networks. Our proposed methodology significantly enhances student performance, elevating the Dice similarity score from 73% to 80%, while the teacher model achieves 81%. Notably, our student model achieves this improvement with only 0.82 million parameters, compared to the teacher model’s 96 million parameters.

Keywords
Ultrasound
Image segmentation
Model compression
Knowledge distillation
Funding
This work was supported by The Natural Sciences and Engineering Research Council of Canada (NSERC) (NSERC RGPIN-2020-04612).
Conflict of interest
The authors declare that they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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