Journal
PROCEEDINGS OF THE IEEE
Volume 108, Issue 1, Pages 30-50Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2019.2949575
Keywords
Biomedical imaging; deep learning
Categories
Funding
- Koc Group
- NSF
- HHMI
Ask authors/readers for more resources
In recent years, deep learning has been shown to be one of the leading machine learning techniques for a wide variety of inference tasks. In addition to its mainstream applications, such as classification, it has created transformative opportunities for image reconstruction and enhancement in optical microscopy. Some of these emerging applications of deep learning range from image transformations between microscopic imaging systems to adding new capabilities to existing imaging techniques, as well as solving various inverse problems based on microscopy image data. Deep learning is helping us move toward data-driven instrument designs that blend microscopy and computing to achieve what neither can do alone. This article provides an overview of some of the recent work using deep neural networks to advance computational microscopy and sensing systems, also covering their current and future biomedical applications.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available