Automatic standardisation of a zebrafish embryo image database

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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Biological image analysis using deep learning-based methods: Literature review

Wang

Shang

Ling

et al. 2018

Digital Medicine

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Automatic standardisation of a zebrafish embryo image database

Cited by 1 publication

References 9 publications

Biological image analysis using deep learning-based methods: Literature review

Biological image analysis using deep learning-based methods: Literature review

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