Deploying deep learning models on unseen medical imaging using adversarial domain adaptation.
The fundamental challenge in machine learning is ensuring that trained models generalize well to unseen data. We developed a general technique for ameliorating the effect of dataset shift using generative adversarial networks (GANs) on a dataset of 149,298 handwritten digits and dataset of 868,549 c...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0273262 |
| _version_ | 1811221676240666624 |
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| author | Aly A Valliani Faris F Gulamali Young Joon Kwon Michael L Martini Chiatse Wang Douglas Kondziolka Viola J Chen Weichung Wang Anthony B Costa Eric K Oermann |
| author_facet | Aly A Valliani Faris F Gulamali Young Joon Kwon Michael L Martini Chiatse Wang Douglas Kondziolka Viola J Chen Weichung Wang Anthony B Costa Eric K Oermann |
| author_sort | Aly A Valliani |
| collection | DOAJ |
| description | The fundamental challenge in machine learning is ensuring that trained models generalize well to unseen data. We developed a general technique for ameliorating the effect of dataset shift using generative adversarial networks (GANs) on a dataset of 149,298 handwritten digits and dataset of 868,549 chest radiographs obtained from four academic medical centers. Efficacy was assessed by comparing area under the curve (AUC) pre- and post-adaptation. On the digit recognition task, the baseline CNN achieved an average internal test AUC of 99.87% (95% CI, 99.87-99.87%), which decreased to an average external test AUC of 91.85% (95% CI, 91.82-91.88%), with an average salvage of 35% from baseline upon adaptation. On the lung pathology classification task, the baseline CNN achieved an average internal test AUC of 78.07% (95% CI, 77.97-78.17%) and an average external test AUC of 71.43% (95% CI, 71.32-71.60%), with a salvage of 25% from baseline upon adaptation. Adversarial domain adaptation leads to improved model performance on radiographic data derived from multiple out-of-sample healthcare populations. This work can be applied to other medical imaging domains to help shape the deployment toolkit of machine learning in medicine. |
| first_indexed | 2024-04-12T08:04:41Z |
| format | Article |
| id | doaj.art-a5ae912379de4f7d8282882fb058543d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T08:04:41Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-a5ae912379de4f7d8282882fb058543d2022-12-22T03:41:12ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011710e027326210.1371/journal.pone.0273262Deploying deep learning models on unseen medical imaging using adversarial domain adaptation.Aly A VallianiFaris F GulamaliYoung Joon KwonMichael L MartiniChiatse WangDouglas KondziolkaViola J ChenWeichung WangAnthony B CostaEric K OermannThe fundamental challenge in machine learning is ensuring that trained models generalize well to unseen data. We developed a general technique for ameliorating the effect of dataset shift using generative adversarial networks (GANs) on a dataset of 149,298 handwritten digits and dataset of 868,549 chest radiographs obtained from four academic medical centers. Efficacy was assessed by comparing area under the curve (AUC) pre- and post-adaptation. On the digit recognition task, the baseline CNN achieved an average internal test AUC of 99.87% (95% CI, 99.87-99.87%), which decreased to an average external test AUC of 91.85% (95% CI, 91.82-91.88%), with an average salvage of 35% from baseline upon adaptation. On the lung pathology classification task, the baseline CNN achieved an average internal test AUC of 78.07% (95% CI, 77.97-78.17%) and an average external test AUC of 71.43% (95% CI, 71.32-71.60%), with a salvage of 25% from baseline upon adaptation. Adversarial domain adaptation leads to improved model performance on radiographic data derived from multiple out-of-sample healthcare populations. This work can be applied to other medical imaging domains to help shape the deployment toolkit of machine learning in medicine.https://doi.org/10.1371/journal.pone.0273262 |
| spellingShingle | Aly A Valliani Faris F Gulamali Young Joon Kwon Michael L Martini Chiatse Wang Douglas Kondziolka Viola J Chen Weichung Wang Anthony B Costa Eric K Oermann Deploying deep learning models on unseen medical imaging using adversarial domain adaptation. PLoS ONE |
| title | Deploying deep learning models on unseen medical imaging using adversarial domain adaptation. |
| title_full | Deploying deep learning models on unseen medical imaging using adversarial domain adaptation. |
| title_fullStr | Deploying deep learning models on unseen medical imaging using adversarial domain adaptation. |
| title_full_unstemmed | Deploying deep learning models on unseen medical imaging using adversarial domain adaptation. |
| title_short | Deploying deep learning models on unseen medical imaging using adversarial domain adaptation. |
| title_sort | deploying deep learning models on unseen medical imaging using adversarial domain adaptation |
| url | https://doi.org/10.1371/journal.pone.0273262 |
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