Deep Learning for Biomedical Image Reconstruction
Thursday, April 11th 2019
Jong Chul Ye, Ph.D. – KAIST Endowed Chair Professor; Professor of Dept. of Bio and Brain Engineering; Korea Advanced Inst. of Science & Technology (KAIST), Daejeon, Republic of Korea
Recently, deep learning approaches have achieved significant performance improvement over existing iterative reconstruction techniques in various biomedical image reconstruction problems. However, it is still unclear to the imaging community why these deep-learning architectures work for specific inverse issues. This tutorial will first discuss the latest state-of-the-art deep-learning image reconstruction algorithms for various imaging modalities such as X-ray CT, MRI, optical imaging, PET, ultrasound, and more. Subsequently, we also introduce recent theoretical results from signal processing community that combines deep learning approaches with the classical signal processing approach, such as compressed sensing, low-rank matrix completion, wavelets, non-local algorithms, etc. In particular, we will show that deep learning approach is a natural extension of compressed sensing theory.
Thursday, April 11th 2019
Jong Chul Ye, Ph.D. – KAIST Endowed Chair Professor; Professor of Dept. of Bio and Brain Engineering; Korea Advanced Inst. of Science & Technology (KAIST), Daejeon, Republic of Korea
Recently, deep learning approaches have achieved significant performance improvement over existing iterative reconstruction techniques in various biomedical image reconstruction problems. However, it is still unclear to the imaging community why these deep-learning architectures work for specific inverse issues. This tutorial will first discuss the latest state-of-the-art deep-learning image reconstruction algorithms for various imaging modalities such as X-ray CT, MRI, optical imaging, PET, ultrasound, and more. Subsequently, we also introduce recent theoretical results from signal processing community that combines deep learning approaches with the classical signal processing approach, such as compressed sensing, low-rank matrix completion, wavelets, non-local algorithms, etc. In particular, we will show that deep learning approach is a natural extension of compressed sensing theory.
