Dual consistent pseudo label generation for multi-source domain adaptation without source data for medical image segmentation
IntroductionUnsupervised domain adaptation (UDA) aims to adapt a model learned from the source domain to the target domain. Thus, the model can obtain transferable knowledge even in target domain that does not have ground truth in this way. In medical image segmentation scenarios, there exist divers...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-06-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1209132/full |
| _version_ | 1797795164801990656 |
|---|---|
| author | Binke Cai Liyan Ma Yan Sun |
| author_facet | Binke Cai Liyan Ma Yan Sun |
| author_sort | Binke Cai |
| collection | DOAJ |
| description | IntroductionUnsupervised domain adaptation (UDA) aims to adapt a model learned from the source domain to the target domain. Thus, the model can obtain transferable knowledge even in target domain that does not have ground truth in this way. In medical image segmentation scenarios, there exist diverse data distributions caused by intensity in homogeneities and shape variabilities. But multi source data may not be freely accessible, especially medical images with patient identity information.MethodsTo tackle this issue, we propose a new multi-source and source-free (MSSF) application scenario and a novel domain adaptation framework where in the training stage, we only get access to the well-trained source domain segmentation models without source data. First, we propose a new dual consistency constraint which uses domain-intra and domain-inter consistency to filter those predictions agreed by each individual domain expert and all domain experts. It can serve as a high-quality pseudo label generation method and produce correct supervised signals for target domain supervised learning. Next, we design a progressive entropy loss minimization method to minimize the class-inter distance of features, which is beneficial to enhance domain-intra and domain-inter consistency in turn.ResultsExtensive experiments are performed for retinal vessel segmentation under MSSF condition and our approach produces impressive performance. The sensitivity metric of our approach is highest and it surpasses other methods with a large margin.DiscussionIt is the first attempt to conduct researches on the retinal vessel segmentation task under multi-source and source-free scenarios. In medical applications, such adaptation method can avoid the privacy issue. Furthermore, how to balance the high sensitivity and high accuracy need to be further considered. |
| first_indexed | 2024-03-13T03:13:45Z |
| format | Article |
| id | doaj.art-359edf2830974eb5818be7180930e72f |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-13T03:13:45Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-359edf2830974eb5818be7180930e72f2023-06-26T06:39:13ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-06-011710.3389/fnins.2023.12091321209132Dual consistent pseudo label generation for multi-source domain adaptation without source data for medical image segmentationBinke CaiLiyan MaYan SunIntroductionUnsupervised domain adaptation (UDA) aims to adapt a model learned from the source domain to the target domain. Thus, the model can obtain transferable knowledge even in target domain that does not have ground truth in this way. In medical image segmentation scenarios, there exist diverse data distributions caused by intensity in homogeneities and shape variabilities. But multi source data may not be freely accessible, especially medical images with patient identity information.MethodsTo tackle this issue, we propose a new multi-source and source-free (MSSF) application scenario and a novel domain adaptation framework where in the training stage, we only get access to the well-trained source domain segmentation models without source data. First, we propose a new dual consistency constraint which uses domain-intra and domain-inter consistency to filter those predictions agreed by each individual domain expert and all domain experts. It can serve as a high-quality pseudo label generation method and produce correct supervised signals for target domain supervised learning. Next, we design a progressive entropy loss minimization method to minimize the class-inter distance of features, which is beneficial to enhance domain-intra and domain-inter consistency in turn.ResultsExtensive experiments are performed for retinal vessel segmentation under MSSF condition and our approach produces impressive performance. The sensitivity metric of our approach is highest and it surpasses other methods with a large margin.DiscussionIt is the first attempt to conduct researches on the retinal vessel segmentation task under multi-source and source-free scenarios. In medical applications, such adaptation method can avoid the privacy issue. Furthermore, how to balance the high sensitivity and high accuracy need to be further considered.https://www.frontiersin.org/articles/10.3389/fnins.2023.1209132/fullunsupervised domain adaptationretinal vessel segmentationsemantic segmentationmulti-sourcesource-free |
| spellingShingle | Binke Cai Liyan Ma Yan Sun Dual consistent pseudo label generation for multi-source domain adaptation without source data for medical image segmentation Frontiers in Neuroscience unsupervised domain adaptation retinal vessel segmentation semantic segmentation multi-source source-free |
| title | Dual consistent pseudo label generation for multi-source domain adaptation without source data for medical image segmentation |
| title_full | Dual consistent pseudo label generation for multi-source domain adaptation without source data for medical image segmentation |
| title_fullStr | Dual consistent pseudo label generation for multi-source domain adaptation without source data for medical image segmentation |
| title_full_unstemmed | Dual consistent pseudo label generation for multi-source domain adaptation without source data for medical image segmentation |
| title_short | Dual consistent pseudo label generation for multi-source domain adaptation without source data for medical image segmentation |
| title_sort | dual consistent pseudo label generation for multi source domain adaptation without source data for medical image segmentation |
| topic | unsupervised domain adaptation retinal vessel segmentation semantic segmentation multi-source source-free |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1209132/full |
| work_keys_str_mv | AT binkecai dualconsistentpseudolabelgenerationformultisourcedomainadaptationwithoutsourcedataformedicalimagesegmentation AT liyanma dualconsistentpseudolabelgenerationformultisourcedomainadaptationwithoutsourcedataformedicalimagesegmentation AT yansun dualconsistentpseudolabelgenerationformultisourcedomainadaptationwithoutsourcedataformedicalimagesegmentation |