DeepHCS++: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening-Reference-Cited by-同舟云学术

DeepHCS++: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening

Published:2021-05 Issue: Volume:70 Page:101995
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Lee Gyuhyun^ORCID,Oh Jeong-Woo,Her Nam-Gu,Jeong Won-Ki

Funder

Korea Health Industry Development Institute

National Research Foundation of Korea

Institute for Information Communication Technology Planning and Evaluation

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference39 articles.

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3. Microscopy-based high-content screening;Boutros;Cell,2015

4. Deep learning to predict microscope images;Brent;Nat. Methods,2018

5. Multitask learning;Caruana;Mach. Learn.,1997

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