%0 Conference Paper %1 conf/isbi/ChanKS21 %A Chan, Emily %A Kelly, Matt %A Schnabel, Julia A. %B ISBI %D 2021 %I IEEE %K dblp %P 729-732 %T Comparison of Classical Machine Learning & Deep Learning to Characterise Fibrosis & Inflammation Using Quantitative MRI. %U http://dblp.uni-trier.de/db/conf/isbi/isbi2021.html#ChanKS21 %@ 978-1-6654-1246-9

@inproceedings{conf/isbi/ChanKS21, added-at = {2021-10-14T00:00:00.000+0200}, author = {Chan, Emily and Kelly, Matt and Schnabel, Julia A.}, biburl = {https://www.bibsonomy.org/bibtex/2cf52d19956c85da761fe41aff453ae37/dblp}, booktitle = {ISBI}, crossref = {conf/isbi/2021}, ee = {https://doi.org/10.1109/ISBI48211.2021.9433962}, interhash = {83b021cb74f098d031ce672b5deffe6b}, intrahash = {cf52d19956c85da761fe41aff453ae37}, isbn = {978-1-6654-1246-9}, keywords = {dblp}, pages = {729-732}, publisher = {IEEE}, timestamp = {2024-04-09T12:26:20.000+0200}, title = {Comparison of Classical Machine Learning & Deep Learning to Characterise Fibrosis & Inflammation Using Quantitative MRI.}, url = {http://dblp.uni-trier.de/db/conf/isbi/isbi2021.html#ChanKS21}, year = 2021 }