GAN, GAN Beyond: From Medical Imaging Reconstruction to Synthesis

[CFAR Distinguished Professor Lecture Series]
GAN, GAN Beyond: From Medical Imaging Reconstruction to Synthesis (Hybrid Event) by Dr Yang Guang
20 Sep 2022 | 2.00pm (Singapore Time)

The computer-assisted analysis for better interpreting images has been a longstanding issue in the medical imaging field. Recent advances in machine learning, especially in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Generative Adversarial Networks (GAN) have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. 

In this talk, Dr Yang Guang from Imperial College London will introduce GAN based methods in medical imaging reconstruction, super-resolution and medical data synthesis. Instead of designing complicated deep neural network architectures for downstream clinical tasks, his research will enhance the importance in medical image data harmonisation and general quality controls to facilitate multi-scanner and multi-centre data collection and analysis. 

Dr Yang Guang is a Future Leaders Fellow (Tenured Advanced Research Fellow) in the National Heart and Lung Institute at Imperial College London. He is also an Honorary Senior Lecturer in the School of Biomedical Engineering & Imaging Sciences at King's College London. His research group is interested in developing novel and translational techniques for imaging and biomedical data analysis. Dr Yang’s group focuses on the research and development of data-driven fast imaging, data harmonisation, image segmentation, image synthesis, federated learning, explainable AI etc. He is currently working on a wide range of clinical applications in cardiovascular disease, lung disease and oncology. Read more on Dr Yang’s Lab at: https://www.yanglab.fyi/.