Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells.
PLoS One
; 16(3): e0246988, 2021.
Article
em En
| MEDLINE
| ID: mdl-33730031
ABSTRACT
Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance in detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and data augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidic-based assays of yeast replicative aging.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Leveduras
/
Processamento de Imagem Assistida por Computador
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Divisão Celular
/
Imagem Molecular
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Dispositivos Lab-On-A-Chip
/
Aprendizado Profundo
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
PLoS One
Assunto da revista:
CIENCIA
/
MEDICINA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos