Barbano, Riccardo;
Denker, Alexander;
Chung, Hyungjin;
Roh, Tae Hoon;
Arridge, Simon;
Maass, Peter;
Jin, Bangti;
(2025)
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction.
IEEE Transactions on Medical Imaging
10.1109/tmi.2024.3524797.
(In press).
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Abstract
Denoising diffusion models have emerged as the go-to generative framework for solving inverse problems in imaging. A critical concern regarding these models is their performance on out-of-distribution tasks, which remains an under-explored challenge. Using a diffusion model on an out-of-distribution dataset, realistic reconstructions can be generated, but with hallucinating image features that are uniquely present in the training dataset. To address this discrepancy and improve reconstruction accuracy, we introduce a novel test-time adaptation sampling framework called Steerable Conditional Diffusion. Specifically, this framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement. Utilising the proposed method, we achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities, advancing the robust deployment of denoising diffusion models in real-world applications.
| Type: | Article |
|---|---|
| Title: | Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tmi.2024.3524797 |
| Publisher version: | https://doi.org/10.1109/tmi.2024.3524797 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. - For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. |
| Keywords: | Neural network, Score-based Generative Models, Image reconstruction, X-ray imaging and computed tomography, Magnetic resonance imaging |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10204072 |
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