Meeting Banner
Abstract #3592

A Further Analysis of Deep Instability in Image Reconstruction

Yue Guan1, Yudu Li2,3, Yao Li1, Yiping Du1, and Zhi-Pei Liang2,3
1Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Deep learning (DL) has emerged as a new tool for solving ill-posed image reconstruction problems and generated a lot of interest in the MRI community. However, image learning is a very high-dimensional problem and deep networks, if not trained properly, would have instability problems. Building upon a recent analysis, we present a further analysis of the instability problems, highlighting: a) the overfitting problem due to limited training data, b) inaccurate density estimation, and c) inadequate sampling from a probability density function. We also present a theoretical analysis of the prediction error based on statistical learning theory.

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here