The Library
Assessing the performance of automated prediction and ranking of patient age from chest X-rays against clinicians
Tools
Tools
Tools
MacPherson, Matthew, Muthuswamy, Keerthini, Amlani, Ashik, Hutchinson, Charles, Goh, Vicky and Montana, Giovanni (2022) Assessing the performance of automated prediction and ranking of patient age from chest X-rays against clinicians. In: 25th International Conference on Medical Image Computing and Computer Assisted Intervention, Singapore, 18-22 Sep 2022. Published in: 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part VII, 13437 pp. 255-265. ISBN 9783031164484. doi:10.1007/978-3-031-16449-1_25 ISSN 0302-9743.
An open access version can be found in:
Official URL: http://dx.doi.org/10.1007/978-3-031-16449-1_25
Abstract
Understanding the internal physiological changes accompanying the aging process is an important aspect of medical image interpretation, with the expected changes acting as a baseline when reporting abnormal findings. Deep learning has recently been demonstrated to allow the accurate estimation of patient age from chest X-rays, and shows potential as a health indicator and mortality predictor. In this paper we present a novel comparative study of the relative performance of radiologists versus state-of-the-art deep learning models on two tasks: (a) patient age estimation from a single chest X-ray, and (b) ranking of two time-separated images of the same patient by age. We train our models with a heterogeneous database of 1.8M chest X-rays with ground truth patient ages and investigate the limitations on model accuracy imposed by limited training data and image resolution, and demonstrate generalisation performance on public data. To explore the large performance gap between the models and humans on these age-prediction tasks compared with other radiological reporting tasks seen in the literature, we incorporate our age prediction model into a conditional Generative Adversarial Network (cGAN) allowing visualisation of the semantic features identified by the prediction model as significant to age prediction, comparing the identified features with those relied on by clinicians.
| Item Type: | Conference Item (Paper) | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
||||||
| Series Name: | Lecture Notes in Computer Science | ||||||
| Journal or Publication Title: | 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part VII | ||||||
| Publisher: | Springer | ||||||
| ISBN: | 9783031164484 | ||||||
| ISSN: | 0302-9743 | ||||||
| Book Title: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 | ||||||
| Official Date: | 17 September 2022 | ||||||
| Dates: |
|
||||||
| Volume: | 13437 | ||||||
| Page Range: | pp. 255-265 | ||||||
| DOI: | 10.1007/978-3-031-16449-1_25 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | 25th International Conference on Medical Image Computing and Computer Assisted Intervention | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Singapore | ||||||
| Date(s) of Event: | 18-22 Sep 2022 | ||||||
| Related URLs: | |||||||
| Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
