Papers › Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images

Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images

1 Apr 2024CVPR 2024 1arXiv:2404.01464archive 2025-07-28

Jungeun Kim, Hangyul Yoon, Geondo Park, KyungSu Kim, Eunho Yang

4D medical images, which represent 3D images with temporal information, are crucial in clinical practice for capturing dynamic changes and monitoring long-term disease progression. However, acquiring 4D medical images poses challenges due to factors such as radiation exposure and imaging duration, necessitating a balance between achieving high temporal resolution and minimizing adverse effects. Given these circumstances, not only is data acquisition challenging, but increasing the frame rate for each dataset also proves difficult. To address this challenge, this paper proposes a simple yet effective Unsupervised Volumetric Interpolation framework, UVI-Net. This framework facilitates temporal interpolation without the need for any intermediate frames, distinguishing it from the majority of other existing unsupervised methods. Experiments on benchmark datasets demonstrate significant improvements across diverse evaluation metrics compared to unsupervised and supervised baselines. Remarkably, our approach achieves this superior performance even when trained with a dataset as small as one, highlighting its exceptional robustness and efficiency in scenarios with sparse supervision. This positions UVI-Net as a compelling alternative for 4D medical imaging, particularly in settings where data availability is limited. The source code is available at https://github.com/jungeun122333/UVI-Net.

PaperPDFConference PDFCode

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

Code

jungeun122333/uvi-net officialmentioned in papermentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

3D Video Frame InterpolationMedical Image GenerationUnsupervised Video Frame Interpolation

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections