Empirical Study of Quality Image Assessment for Synthesis of Fetal Head Ultrasound Imaging with DCGANs
Not on the map. Why?
Abstract (arXiv)
In this work, we present an empirical study of DCGANs, including hyperparameter heuristics and image quality assessment, as a way to address the scarcity of datasets to investigate fetal head ultrasound. We present experiments to show the impact of different image resolutions, epochs, dataset size input, and learning rates for quality image assessment on four metrics: mutual information (MI), Fr'echet inception distance (FID), peak-signal-to-noise ratio (PSNR), and local binary pattern vector (LBPv). The results show that FID and LBPv have stronger relationship with clinical image quality scores. The resources to reproduce this work are available at urlhttps://github.com/budai4medtech/miua2022.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 1 Jun 2022.
Builds on
6 of 13 references.
References · 6
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
- 2018GANs for Medical Image Analysiscited by 81
- 2021GANs for Medical Image Synthesis: An Empirical Studycited by 64
- 2022A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysiscited by 29
- 2017Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networkscited by 15
- 2017Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learningcited by 13
Built on by
Citing papers per year: 2023: 1.
1 paper.