3 papers
cs.CV2026
CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimation
Yousra Nabila Taifour, Marouane Tliba, Zuheng Ming +9
Computed tomography (CT) images are frequently degraded by acquisition artifacts, including noise, blur, streaking, aliasing, and metal artifacts. Yet CT enhancement is still large…
eess.IV2025
Comparative clinical evaluation of "memory-efficient" synthetic 3d generative adversarial networks (gan) head-to-head to state of art: results on computed tomography of the chest
Mahshid Shiri, Chandra Bortolotto, Alessandro Bruno +5
Generative Adversarial Networks (GANs) are increasingly used to generate synthetic medical images, addressing the critical shortage of annotated data for training Artificial Intell…
cs.CV2025
Memory-Efficient 3D High-Resolution Medical Image Synthesis Using CRF-Guided GANs
Mahshid Shiri, Alessandro Bruno, Daniele Loiacono
Generative Adversarial Networks (GANs) have many potential medical imaging applications. Due to the limited memory of Graphical Processing Units (GPUs), most current 3D GAN models…