ArticlePloS one2025
MeVGAN: GAN-based plugin model for video generation with applications in colonoscopy.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- InfoColon: A dataset for consecutive informative frames in Colonoscopy.Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The generation of videos is crucial, particularly in the medical field, where a significant amount of data is presented in this format. However, due to the extensive memory requirements, creating high-resolution videos poses a substantial challenge for generative models. In this paper, we introduce the Memory Efficient Video GAN (MeVGAN)-a Generative Adversarial Network (GAN) that incorporates a plugin-type architecture. This system utilizes a pre-trained 2D-image GAN, to which we attach a straightforward neural network designed to develop specific trajectories within the noise space. These trajectories, when processed through the GAN, produce realistic videos. We deploy MeVGAN specifically for creating colonoscopy videos, a critical procedure in the medical field, notably helpful for screening and treating colorectal cancer. We show that MeVGAN can produce good quality synthetic colonoscopy videos, which can be potentially used in virtual simulators.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.