Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham.
In this work, we introduce a novel deep learning architecture, MUCRAN (Multi-Confound Regression Adversarial Network), to train a deep learning model on clinical brain MRI while regressing demographic and technical confounding factors. We trained MUCRAN using 17,076 clinical T1 Axial brain MRIs coll...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0277572&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832595666572935168 |
|---|---|
| author | Matthew Leming Sudeshna Das Hyungsoon Im |
| author_facet | Matthew Leming Sudeshna Das Hyungsoon Im |
| author_sort | Matthew Leming |
| collection | DOAJ |
| description | In this work, we introduce a novel deep learning architecture, MUCRAN (Multi-Confound Regression Adversarial Network), to train a deep learning model on clinical brain MRI while regressing demographic and technical confounding factors. We trained MUCRAN using 17,076 clinical T1 Axial brain MRIs collected from Massachusetts General Hospital before 2019 and demonstrated that MUCRAN could successfully regress major confounding factors in the vast clinical dataset. We also applied a method for quantifying uncertainty across an ensemble of these models to automatically exclude out-of-distribution data in AD detection. By combining MUCRAN and the uncertainty quantification method, we showed consistent and significant increases in the AD detection accuracy for newly collected MGH data (post-2019; 84.6% with MUCRAN vs. 72.5% without MUCRAN) and for data from other hospitals (90.3% from Brigham and Women's Hospital and 81.0% from other hospitals). MUCRAN offers a generalizable approach for deep-learning-based disease detection in heterogenous clinical data. |
| format | Article |
| id | doaj-art-29d151909ec048ea8e788741eef71cd8 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-29d151909ec048ea8e788741eef71cd82025-01-18T05:31:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01183e027757210.1371/journal.pone.0277572Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham.Matthew LemingSudeshna DasHyungsoon ImIn this work, we introduce a novel deep learning architecture, MUCRAN (Multi-Confound Regression Adversarial Network), to train a deep learning model on clinical brain MRI while regressing demographic and technical confounding factors. We trained MUCRAN using 17,076 clinical T1 Axial brain MRIs collected from Massachusetts General Hospital before 2019 and demonstrated that MUCRAN could successfully regress major confounding factors in the vast clinical dataset. We also applied a method for quantifying uncertainty across an ensemble of these models to automatically exclude out-of-distribution data in AD detection. By combining MUCRAN and the uncertainty quantification method, we showed consistent and significant increases in the AD detection accuracy for newly collected MGH data (post-2019; 84.6% with MUCRAN vs. 72.5% without MUCRAN) and for data from other hospitals (90.3% from Brigham and Women's Hospital and 81.0% from other hospitals). MUCRAN offers a generalizable approach for deep-learning-based disease detection in heterogenous clinical data.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0277572&type=printable |
| spellingShingle | Matthew Leming Sudeshna Das Hyungsoon Im Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham. PLoS ONE |
| title | Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham. |
| title_full | Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham. |
| title_fullStr | Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham. |
| title_full_unstemmed | Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham. |
| title_short | Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham. |
| title_sort | adversarial confound regression and uncertainty measurements to classify heterogeneous clinical mri in mass general brigham |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0277572&type=printable |
| work_keys_str_mv | AT matthewleming adversarialconfoundregressionanduncertaintymeasurementstoclassifyheterogeneousclinicalmriinmassgeneralbrigham AT sudeshnadas adversarialconfoundregressionanduncertaintymeasurementstoclassifyheterogeneousclinicalmriinmassgeneralbrigham AT hyungsoonim adversarialconfoundregressionanduncertaintymeasurementstoclassifyheterogeneousclinicalmriinmassgeneralbrigham |