Cited by IAMS-Net: An Illumination-Adaptive Multi-Scale Lesion Segmentation Network

Brain Causality Modeling Using Structure-Guided Spatiotemporal Diffusion Model for MCI Analysis DOI

Yanfei Zhu,

Jiangtao Wang, Xuan Cheng

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 3 - 11

Published: Jan. 1, 2025

Language: Английский

Citations

Generative Adversarial Networks in Brain Imaging: A Decade-Long Review of Progress and Future Directions DOI

Hala Shaari

Journal of Pure & Applied Sciences, Journal Year: 2025, Volume and Issue: 24(1), P. 73 - 103

Published: April 11, 2025

Due to the increasing demand for effective and objective analysis address complex challenges such as brain medical image reconstruction, segmentation, classification, tumor research has gained significant attention. The ability of Generative Adversarial Networks (GANs) increase probability density over data distributions by estimating ratios, along with their capacity uncover high-dimensional latent distributions, led substantial performance improvements in visual feature extraction. Furthermore, adversarial loss incurred discriminator offers a subtle method incorporating unlabeled samples into training, thereby improving accuracy at higher orders. These characteristics GANs have proven valuable various applications, including enhancing images translating across different modalities. Additionally, generate remarkable realism hope that, through these generative models, ongoing challenge limited labelled field may be overcome. aim this review is provide comprehensive overview, starting concise summary range available GAN architectures datasets. This study then highlights conducted processing interpreting GAN-based images. Finally, limitations methods are discussed, identifying unresolved issues suggesting avenues further exploration emerging field.

Language: Английский

Citations

Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation DOI

Yulin Chen, Guoheng Huang, Kai Huang

et al.

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Journal Year: 2024, Volume and Issue: unknown, P. 2255 - 2260

Published: Oct. 6, 2024

Language: Английский

Citations

IAMS-Net: An Illumination-Adaptive Multi-Scale Lesion Segmentation Network DOI

Yefeng Zheng, Guoheng Huang, Feng Zhang

et al.

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Journal Year: 2024, Volume and Issue: unknown, P. 4425 - 4430

Published: Oct. 6, 2024

Language: Английский

Citations