We present a unified overview of biomedical image reconstruction via direct, variational, and learning-based methods. We start with a review of linear reconstruction methods (first generation) that typically involve some form of back-propagation (CT or PET) and/or the fast Fourier transform (in the case of MRI). We then move on to sparsity-promoting reconstructions algorithms supported by the...
In recent years, several imaging fields, including computed
tomography, have benefited from the use of deep learning methods. Nonetheless, successful practical application of these
techniques is often inhibited by the lack of sufficient training data. In this talk, we present several approaches for applying deep neural networks to tomography problems where little or no training data is...
Structural bioinformatics and structural biology in the last 40 years have been dominated by bottom-up approaches. Specifically, researchers have been trying to construct complex models of macromolecules starting from the first principles. These approaches require many approximations and very often turned out to be rough or even incorrect. For example, many classical methods are based on a...
Analysis of massive spatial sensor datasets has been revolutionized by the advent of neural network methods. This class of methods has enabled identification and classification of spatio-temporal patterns and objects at multiple scales. Neural network methods need to be trained; the disparity between the very limited human ability to generate training data and the overwhelming training data...
The High Energy Physics (HEP) community has a long tradition of using Machine Learning methods to solve tasks related, mostly, to the selection of interesting events over the overwhelming background produced at colliders. In recent years, several studies, in different fields of science, industry and society, have demonstrated the benefit of using Deep Learning (DL) to solve typical tasks...
