Test-Time Adaptation in Optical Coherence Tomography Using Trajectory-Aligned Time-Independent Flow
arXiv:2606.18876v1 Announce Type: cross Abstract: Optical coherence tomography (OCT) is essential in ophthalmology, but inconsistent image quality especially in low-cost devices hinders automated analysis. To address this, we introduce a flow-matching-based test-time adaptation method that generates high-quality surrogate images from noisy inputs. Typically, domain gaps between test and training data cause pixel distribution mismatches during the denoising process. We overcome this by matching the test image's histogram to synthetic reference trajectories, successfully aligning the input with
The proliferation of AI in medical imaging necessitates robust solutions for inconsistent data quality, aligning with ongoing research to improve diagnostic accuracy.
This development can significantly enhance the reliability of AI-driven medical diagnostics, particularly in resource-constrained settings using low-cost devices.
The ability to generate high-quality surrogate images from noisy inputs at test time reduces dependency on pristine training data distributions for AI models in medical imaging.
- · Ophthalmology device manufacturers
- · Healthcare AI developers
- · Patients in developing regions
- · Medical diagnostic services
- · Manufacturers of high-cost, high-precision OCT devices if low-cost alternatives
Improved diagnostic accuracy in ophthalmology through enhanced image quality from low-cost OCT devices.
Wider adoption of AI in ophthalmology, potentially leading to earlier disease detection and more effective treatment plans.
Democratization of advanced medical diagnostics in regions where expensive, high-quality imaging equipment is not feasible.
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Read at arXiv cs.LG