Biological image analysis using deep learning-based methods: Literature review
| Autor: | Ling Long, Yi Wu, Shang Shang, Fengyu Cong, Na Chen, Hongkai Wang, Ruxue Hu, Sijie Lin, Shaoxiang Zhang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Training set Contextual image classification business.industry Image quality Computer science Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Feature selection Pattern recognition Image segmentation Object detection 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Artificial intelligence business |
| Zdroj: | Digital Medicine. 4:157 |
| ISSN: | 2226-8561 |
| DOI: | 10.4103/digm.digm_16_18 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|