Comparison of classical machine learning deep learning to characterise fibrosis inflammation using quantitative MRI
Chan E, Kelly M, Schnabel JA (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: IEEE Computer Society
Book Volume: 2021-April
Pages Range: 729-732
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Nice, FRA
ISBN: 9781665412469
DOI: 10.1109/ISBI48211.2021.9433962
Abstract
The quantitative MRI metric, T1, has been used to characterise fibroinflammation in the liver; however, the T1 value alone is unable to differentiate between fibrosis and inflammation. We evaluate the potential utility of classical machine learning techniques (K-Nearest Neighbours, Support Vector Machine and Random Forest) to address this problem using information in the T1 map. We also compare to transfer learning, utilising multiple methods to alleviate the effects of class imbalance. Random Forest with Adaptive Synthetic Sampling was superior to mean T1 in categorising fibroinflammation. Despite the relatively small number of samples (n=289) and large class imbalance, our results demonstrate potential in using the whole T1 map with machine learning for this task.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Chan, E., Kelly, M., & Schnabel, J.A. (2021). Comparison of classical machine learning deep learning to characterise fibrosis inflammation using quantitative MRI. In Proceedings - International Symposium on Biomedical Imaging (pp. 729-732). Nice, FRA: IEEE Computer Society.
MLA:
Chan, Emily, Matt Kelly, and Julia A. Schnabel. "Comparison of classical machine learning deep learning to characterise fibrosis inflammation using quantitative MRI." Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021, Nice, FRA IEEE Computer Society, 2021. 729-732.
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