Conference item
SFHarmony: source free domain adaptation for distributed neuroimaging analysis
- Abstract:
- To represent the biological variability of clinical neuroimaging populations, it is vital to be able to combine data across scanners and studies. However, different MRI scanners produce images with different characteristics, resulting in a domain shift known as the ‘harmonisation problem’. Additionally, neuroimaging data is inherently personal in nature, leading to data privacy concerns when sharing the data. To overcome these barriers, we propose an Unsupervised Source-Free Domain Adaptation (SFDA) method, SFHarmony. Through modelling the imaging features as a Gaussian Mixture Model and minimising an adapted Bhattacharyya distance between the source and target features, we can create a model that performs well for the target data whilst having a shared feature representation across the data domains, without needing access to the source data for adaptation or target labels. We demonstrate the performance of our method on simulated and real domain shifts, showing that the approach is applicable to classification, segmentation and regression tasks, requiring no changes to the algorithm. Our method outperforms existing SFDA approaches across a range of realistic data scenarios, demonstrating the potential utility of our approach for MRI harmonisation and general SFDA problems. Our code is available at https://github.com/nkdinsdale/SFHarmony.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 11.3MB, Terms of use)
-
- Publisher copy:
- 10.1109/ICCV51070.2023.01056
Authors
- Publisher:
- IEEE
- Host title:
- 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
- Pages:
- 11460-11471
- Publication date:
- 2024-01-14
- Acceptance date:
- 2023-07-14
- Event title:
- International Conference on Computer Vision (ICCV 2023)
- Event location:
- Paris, France
- Event website:
- https://iccv2023.thecvf.com/
- Event start date:
- 2023-10-02
- Event end date:
- 2023-10-06
- DOI:
- EISSN:
-
2380-7504
- ISSN:
-
1550-5499
- EISBN:
- 9798350307184
- ISBN:
- 9798350307191
- Language:
-
English
- Pubs id:
-
1528845
- Local pid:
-
pubs:1528845
- Deposit date:
-
2023-09-14
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2023
- Rights statement:
- © 2023 IEEE.
- Notes:
- This is the accepted manuscript version of the paper. The final version is available online from IEEE at https://dx.doi.org/10.1109/ICCV51070.2023.01056
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