Paper Info
Title
A Transfer Learning Based Super-Resolution Microscopy for Biopsy Slice Images: The Joint Methods Perspective
Abstract
AbstractHigher-resolution biopsy slice images reveal many details, which are widely used in medical practice. However, taking high-resolution slice images is more costly than taking low-resolution ones. In this paper, we propose a joint framework containing a novel transfer learning strategy and a deep super-resolution framework to generate high-resolution slice images from low-resolution ones. The super-resolution framework called SRFBN+ is proposed by modifying a state-of-the-art framework SRFBN. Specifically, the structure of the feedback block of SRFBN was modified to be more flexible. Besides, it is challenging to use typical transfer learning strategies directly for the tasks on slice images, as the patterns on different types of biopsy slice images are varying. To this end, we propose a novel transfer learning strategy, called Channel Fusion Transfer Learning (CF-Trans). CF-Trans builds a middle domain by fusing the data manifolds of the source domain and the target domain, serving as a springboard for knowledge transfer. Thus, in the transfer learning setting, SRFBN+ can be trained on the source domain and then the middle domain and finally the target domain. Experiments on biopsy slice images validate SRFBN+ works well in generating super-resolution slice images, and CF-Trans is an efficient transfer learning strategy.
Year
DOI
Venue
2021
10.1109/TCBB.2020.2991173
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Keywords
DocType
Volume
Transfer learning, super-resolution techniques, image reconstruction, biopsy slices
Journal
18
Issue
ISSN
Citations 
1
1545-5963
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Jintai Chen144.12
Haochao Ying27310.03
Xuechen Liu300.34
Jingjing Gu4418.26
Ruiwei Feng524.41
Tingting Chen684.49
Honghao Gao721745.24
Jian Wu893395.62