ArticleMedical & biological engineering & computing2019
Fluorescence microscopy image classification of 2D HeLa cells based on the CapsNet neural network.
Article in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- LMFD: lightweight multi-feature descriptors for image stitching.Scientific reports · 2023Article
- VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images.Scanning · 2022Article
- Cell Phenotype Classification Based on Joint of Texture Information and Multilayer Feature Extraction in DenseNet.Computational intelligence and neuroscience · 2022Article
- Deep learning classification of cervical dysplasia using depth-resolved angular light scattering profiles.Biomedical optics express · 2021Article
- Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells.PloS one · 2021Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
The development of computer technology now allows the quick and efficient automatic fluorescence microscopy generation of a large number of images of proteins in specific subcellular compartments using fluorescence microscopy. Digital image processing and pattern recognition technology can easily classify these images, identify the subcellular location of proteins, and subsequently carry out related work such as analysis and investigation of protein function. Here, based on a fluorescence microscopy 2D image dataset of HeLa cells, the CapsNet network model was used to classify ten types of images of proteins in different subcellular compartments. Capsules in the CapsNet network model were trained to capture the possibility of certain features and variants rather than to capture the characteristics of a specific variant. The capsule at the same level predicted the instantiation parameters of the higher level capsule through the transformation matrix, and the higher level capsule became active when multiple dynamic routing forecasts were consistent. Experiments show that using the CapsNet network model to classify 2D HeLa datasets can achieve higher accuracy. Graphical abstract ᅟ.
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