ArticleJournal of imaging2021
Conditional Invertible Neural Networks for Medical Imaging.
Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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Who cites it
6 citing papers in PubMed.
- PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification.Journal of imaging informatics in medicine · 2025Article
- Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2025Article
- Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Fast light-field 3D microscopy with out-of-distribution detection and adaptation through conditional normalizing flows.Biomedical optics express · 2024Article
- Inverse design of optical lenses enabled by generative flow-based invertible neural networks.Scientific reports · 2023Article
- A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging.Proceedings of machine learning research · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Over recent years, deep learning methods have become an increasingly popular choice for solving tasks from the field of inverse problems. Many of these new data-driven methods have produced impressive results, although most only give point estimates for the reconstruction. However, especially in the analysis of ill-posed inverse problems, the study of uncertainties is essential. In our work, we apply generative flow-based models based on invertible neural networks to two challenging medical imaging tasks, i.e., low-dose computed tomography and accelerated medical resonance imaging. We test different architectures of invertible neural networks and provide extensive ablation studies. In most applications, a standard Gaussian is used as the base distribution for a flow-based model. Our results show that the choice of a radial distribution can improve the quality of reconstructions.
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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.