Droste, R; Cai, Y; Sharma, H; Chatelain, P; Drukker, L; Papageorghiou, AT; Noble, JA
(2019)
Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention.
In: 26th International Conference on Information Processing in Medical Imaging (IPMI), Inf Process Med Imaging, June 2-7 2019, Hong Kong University of Science and Technology.
SGUL Authors: Papageorghiou, Aris
![[img]](https://openaccess.sgul.ac.uk/111944/1.hassmallThumbnailVersion/droste-et-al_ultrasound-visual-attention-modeling.pdf) ![](https://openaccess.sgul.ac.uk/111944/1.haspreviewThumbnailVersion/droste-et-al_ultrasound-visual-attention-modeling.pdf) Preview |
|
PDF
Accepted Version
Available under License ["licenses_description_publisher" not defined].
Download (3MB)
| Preview
|
Abstract
Image representations are commonly learned from class labels, which are a simplistic approximation of human image understanding. In this paper we demonstrate that transferable representations of images can be learned without manual annotations by modeling human visual attention. The basis of our analyses is a unique gaze tracking dataset of sonographers performing routine clinical fetal anomaly screenings. Models of sonographer visual attention are learned by training a convolutional neural network (CNN) to predict gaze on ultrasound video frames through visual saliency prediction or gaze-point regression. We evaluate the transferability of the learned representations to the task of ultrasound standard plane detection in two contexts. Firstly, we perform transfer learning by fine-tuning the CNN with a limited number of labeled standard plane images. We find that fine-tuning the saliency predictor is superior to training from random initialization, with an average F1-score improvement of 9.6% overall and 15.3% for the cardiac planes. Secondly, we train a simple softmax regression on the feature activations of each CNN layer in order to evaluate the representations independently of transfer learning hyper-parameters. We find that the attention models derive strong representations, approaching the precision of a fully-supervised baseline model for all but the last layer.
Item Type: |
Conference or Workshop Item
(Poster)
|
Additional Information: |
This is a post-peer-review, pre-copyedit version of an article published in Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science, vol 11492. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-20351-1_46 |
Keywords: |
Convolutional neural networks, Fetal ultrasound, Gaze tracking, Representation learning, Saliency prediction, Self-supervised learning, Transfer learning, Representation learning, Gaze tracking, Fetal ultrasound, Self-supervised learning, Saliency prediction, Transfer learning, Convolutional neural networks |
SGUL Research Institute / Research Centre: |
Academic Structure > Institute of Medical & Biomedical Education (IMBE) Academic Structure > Institute of Medical & Biomedical Education (IMBE) > Centre for Clinical Education (INMECE ) |
Journal or Publication Title: |
Inf Process Med Imaging |
ISSN: |
1011-2499 |
Dates: |
Date | Event |
---|
June 2019 | Published | 22 May 2019 | Published Online | 26 February 2019 | Accepted |
|
Publisher License: |
Publisher's own licence |
Projects: |
|
PubMed ID: |
31992944 |
Web of Science ID: |
WOS:000493380900046 |
![](http://static.pubmed.gov/portal/portal3rc.fcgi/2745237/img/27842) |
Go to PubMed abstract |
URI: |
https://openaccess.sgul.ac.uk/id/eprint/111944 |
Publisher's version: |
https://doi.org/10.1007/978-3-030-20351-1_46 |
Statistics
Item downloaded times since 13 May 2020.
Actions (login required)
![Edit Item](/style/images/action_view.png) |
Edit Item |