Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are 1000×1000×1000times larger in size.
@inproceedings{patra_efficient_2019,
title = {Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation},
author = {Patra, Arijit and Cai, Yifan and Chatelain, Pierre and Sharma, Harshita and Drukker, Lior and Papageorghiou, Aris T. and Noble, J. Alison},
booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2019},
doi = {10.1007/978-3-030-32251-9_43},
editor = {Shen, Dinggang and Liu, Tianming and Peters, Terry M. and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean and Yap, Pew-Thian and Khan, Ali},
isbn = {978-3-030-32251-9},
language = {en},
pages = {394--402},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
address = {Cham},
year = {2019}
}