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Biological image analysis using deep learning-based methods: Literature review
Hongkai Wang1, Shang Shang1, Ling Long2, Ruxue Hu3, Yi Wu4, Na Chen4, Shaoxiang Zhang4, Fengyu Cong1, Sijie Lin2
1 School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning Province, China
2 College of Environmental Science and Engineering, Tongji University, Shanghai, China
3 Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
4 Institute of Digital Medicine, Biomedical Engineering College, Third Military Medical University, Chongqing, China
Correspondence Address:
Fengyu Cong
Faculty of Electronic Information and Electrical Engineering, School of Biomedical Engineering, Dalian University of Technology, No. 2, Linggong Road, Dalian 116024, Liaoning Province
China
Sijie Lin
College of Environmental Science and Engineering, Tongji University, Shanghai 200092
China
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/digm.digm_16_18
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Automatic processing large amount of microscopic images is important for medical and biological studies. Deep learning has demonstrated better performance than traditional machine learning methods for processing massive quantities of images; therefore, it has attracted increasing attention from the research and industry fields. This paper summarizes the latest progress of deep learning methods in biological microscopic image processing, including image classification, object detection, and image segmentation. Compared to the traditional machine learning methods, deep neural networks achieved better accuracy without tedious feature selection procedure. Obstacles of the biological image analysis with deep learning methods include limited training set and imperfect image quality. Viable solutions to these obstacles are discussed at the end of the paper. With this survey, we hope to provide a reference for the researchers conducting biological microscopic image processing.
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