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):
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