Simulation of biomedical signals and images using Monte Carlo methods for training of deep learning networks
Document Type
Book Chapter
Publication Title
Deep Learning Techniques for Biomedical and Health Informatics
First Page
213
Last Page
236
Publisher
Elsevier
School
Electron Science Research Institute
RAS ID
35302
Abstract
© 2020 Elsevier Inc. All rights reserved. High accuracy supervised deep learning methods require massive data with accurate ground truth. However, in biomedical applications, exact measurement of the ground truth is often impractical or even impossible. An important avenue to generate data with ground truth is simulation of the biomedical or imaging process. In this chapter, Monte Carlo methods are proposed as a useful set of tools to generate physics-based simulated signals and images.
DOI
10.1016/B978-0-12-819061-6.00009-4
Access Rights
subscription content
Comments
Mavaddat, N., Ahderom, S., Tiporlini, V., & Alameh, K. (2020). Simulation of biomedical signals and images using Monte Carlo methods for training of deep learning networks. In B. Agarwal, V. E. Balas, L. C. Jain, R. C. Poonia & Manisha (Eds.), Deep Learning Techniques for Biomedical and Health Informatics (pp. 213-236). Elsevier. https://doi.org/10.1016/B978-0-12-819061-6.00009-4