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Chan, E.* ; Kelly, M.* ; Schnabel, J.A.*

Comparison of classical machine learning deep learning to characterise fibrosis inflammation using quantitative MRI.

In: (2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 13-16 April 2021, Nice, France). 2021. 729-732 (Proceedings - International Symposium on Biomedical Imaging ; 2021-April)
DOI
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.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Knn ; Liver ; Random Forests ; Svm ; T1 Mapping ; Transfer Learning ; Wideresnet
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Konferenztitel 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Konferzenzdatum 13-16 April 2021
Konferenzort Nice, France
Quellenangaben Band: 2021-April, Heft: , Seiten: 729-732 Artikelnummer: , Supplement: ,
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)