Rakowski, A.G., Veličković, P., Dall’Ara, E. et al. (1 more author) (2020) ChronoMID—Cross-modal neural networks for 3-D temporal medical imaging data. PLoS ONE, 15 (2). e0228962. ISSN 1932-6203
Abstract
ChronoMID—neural networks for temporally-varying, hence Chrono, Medical Imaging Data—makes the novel application of cross-modal convolutional neural networks (X-CNNs) to the medical domain. In this paper, we present multiple approaches for incorporating temporal information into X-CNNs and compare their performance in a case study on the classification of abnormal bone remodelling in mice. Previous work developing medical models has predominantly focused on either spatial or temporal aspects, but rarely both. Our models seek to unify these complementary sources of information and derive insights in a bottom-up, data-driven approach. As with many medical datasets, the case study herein exhibits deep rather than wide data; we apply various techniques, including extensive regularisation, to account for this. After training on a balanced set of approximately 70000 images, two of the models—those using difference maps from known reference points—outperformed a state-of-the-art convolutional neural network baseline by over 30pp (> 99% vs. 68.26%) on an unseen, balanced validation set comprising around 20000 images. These models are expected to perform well with sparse data sets based on both previous findings with X-CNNs and the representations of time used, which permit arbitrarily large and irregular gaps between data points. Our results highlight the importance of identifying a suitable description of time for a problem domain, as unsuitable descriptors may not only fail to improve a model, they may in fact confound it.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2020 Rakowski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Human Metabolism (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Oncology (Sheffield) |
Funding Information: | Funder Grant number NATIONAL CENTRE FOR THE REPLACEMENT, REFINEMENT AND REDUCTION OF ANIMALS IN RESEARCH NC/R001073/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/K03877X/1 Engineering and Physical Sciences Research Council EP/K03877X/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Apr 2020 16:19 |
Last Modified: | 15 Jul 2020 12:53 |
Status: | Published |
Publisher: | Public Library of Science (PLoS) |
Refereed: | Yes |
Identification Number: | 10.1371/journal.pone.0228962 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158898 |
Download
Filename: ChronoMID-Cross-modal neural networks for 3-D temporal medical imaging data.pdf
Licence: CC-BY 4.0