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Knowledge distillation for efficient standard scanplane detection of fetal ultrasound
Abstract In clinical practice, ultrasound standard planes (SPs) selection is experience-dependent and it suffers from inter-observer and intra-observer variability. Automatic recognition of SPs can help improve the quality of examinations and make the evaluations more objective. In this paper, we propose a method for the automatic identification of SPs, to be installed onboard a portable ultrasound system with limited computational power. The deep Learning methodology we design is based on the concept of Knowledge Distillation, transferring knowledge from a large and well-performing teacher to a smaller student architecture. To this purpose, we evaluate a set of different potential teachers and students, as well as alternative knowledge distillation techniques, to balance a trade-off between performances and architectural complexity. We report a thorough analysis of fetal ultrasound data, focusing on a benchmark dataset, to the best of our knowledge the only one available to date. Graphical abstract
Knowledge distillation for efficient standard scanplane detection of fetal ultrasound
Abstract In clinical practice, ultrasound standard planes (SPs) selection is experience-dependent and it suffers from inter-observer and intra-observer variability. Automatic recognition of SPs can help improve the quality of examinations and make the evaluations more objective. In this paper, we propose a method for the automatic identification of SPs, to be installed onboard a portable ultrasound system with limited computational power. The deep Learning methodology we design is based on the concept of Knowledge Distillation, transferring knowledge from a large and well-performing teacher to a smaller student architecture. To this purpose, we evaluate a set of different potential teachers and students, as well as alternative knowledge distillation techniques, to balance a trade-off between performances and architectural complexity. We report a thorough analysis of fetal ultrasound data, focusing on a benchmark dataset, to the best of our knowledge the only one available to date. Graphical abstract
Knowledge distillation for efficient standard scanplane detection of fetal ultrasound
Med Biol Eng Comput
Dapueto, Jacopo (author) / Zini, Luca (author) / Odone, Francesca (author)
Medical & Biological Engineering & Computing ; 62 ; 73-82
2024-01-01
Article (Journal)
Electronic Resource
English
Correction to: Knowledge distillation for efficient standard scanplane detection of fetal ultrasound
| Springer Verlag | 2024
| Springer Verlag | 2024
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