Deep Dive to Image Generation for Medical Imaging

, Postdoc Research Fellow, Mayo Clinic (Rochester, Minnesota)
, Postdoc Research Fellow, Mayo Clinic (Rochester, Minnesota)
, Medical Doctor and Research Fellow, Mayo Clinic (Rochester, Minnesota)
, Research Associate, Mayo Clinic (Rochester, Minnesota)

Despite the ever-increasing interest in applying deep learning (DL) models to medical imaging, the typical scarcity and imbalance of medical datasets can severely impact the performance of DL models. The generation of synthetic data that might be freely shared without compromising patient privacy is a well-known technique for addressing these difficulties. This lab teaches: Different generative models Variational autoencoders (VAEs) Generative adversarial networks (GANs) Denoising diffusion probabilistic models (DDPMs) Intuitions behind different generative models Training objective of generative models You will implement the VAE, GAN and DDPM using PyTorch and MONAI. 

 

Prerequisite(s): 

  • Familiarity with PyTorch and Computer vision's concepts. 

 

Please disregard any reference to "Event Code" for access to training materials. "Event Codes" are only valid during the original live session. Explore more training options offered by the NVIDIA Deep Learning Institute (DLI). Choose from an extensive catalog of self-paced, online courses or instructor-led virtual workshops to help you develop key skills in AI, HPC, graphics & simulation, and more.

活动: GTC Digital Spring
日期: March 2023
话题: Deep Learning - Training
行业: 医疗健康与生命科学
级别: 中级技术
语言: 英语
话题: Deep Learning
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