Unsupervised Deep Learning Methods for Biological Image Reconstruction

05/17/2021 ∙ by Mehmet Akcakaya, et al. ∙ 157

Recently, deep learning approaches have become the main research frontier for biological image reconstruction problems thanks to their high performance, along with their ultra-fast reconstruction times. However, due to the difficulty of obtaining matched reference data for supervised learning, there has been increasing interest in unsupervised learning approaches that do not need paired reference data. In particular, self-supervised learning and generative models have been successfully used for various biological imaging applications. In this paper, we overview these approaches from a coherent perspective in the context of classical inverse problems, and discuss their applications to biological imaging.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 10

page 12

page 13

page 23

page 24

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.