Author:
Dapueto Jacopo,Zini Luca,Odone Francesca
Abstract
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
Funder
Università degli Studi di Genova
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Biomedical Engineering