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Artificial classification of cervical squamous lesions in ThinPrep cytologic tests using a deep convolutional neural network
- Source :
- Oncology Letters
- Publication Year :
- 2020
-
Abstract
- The diagnosis of squamous cell carcinoma requires the accurate classification of cervical squamous lesions in the ThinPrep cytologic test (TCT). It primarily relies on a pathologist's interpretation under a microscope. Deep convolutional neural networks (DCNN) have played an increasingly important role in digital pathology. However, they have not been applied to diverse datasets and externally validated. In the present study, a DCNN model based on VGG16 and an ensemble training strategy (ETS) based on 5-fold cross-validation was employed to automatically classify normal and abnormal cervical squamous cells from a multi-center dataset. First, we collected a dataset comprising 82 TCT samples from four hospitals and fine-tuned our model twice on the dataset with and without the ETS. Then, we compared the classifications obtained from the models with those provided by two skilled pathologists to discriminate the performance of the models in terms of classification accuracy and efficiency. Finally, paired sample t-tests were used to validate the consistency between the classification provided by the proposed methods and that of the pathologists. The results showed that ETS slightly, though not significantly, improved the classification accuracy compared with that of the pathologists: P0=0.387>0.05 (DCNN without ETS vs. DCNN with ETS), P1=0.771>0.05 (DCNN with ETS vs. pathologist 1), P2=0.489>0.05 (DCNN with ETS vs. pathologist 2). The DCNN model was almost 6-fold faster than that of the pathologists. The accuracy of our automated scheme was similar to that of the pathologists, but a higher efficiency in the accurate identification of cervical squamous lesions was provided by the scheme. This result allows for wider and more efficient screening and may provide a replacement for pathologists in the future. Future research should address the viability of the practical implementation of such DCNN models in the laboratory setting.
- Subjects :
- 0301 basic medicine
squamous cell carcinoma
training strategy
Cancer Research
Computer science
cervical cancer
ThinPrep cytologic test
cell classification
convolutional neural network
Convolutional neural network
03 medical and health sciences
0302 clinical medicine
Paired samples
medicine
Basal cell
Cytologic Test
Cervical cancer
business.industry
Digital pathology
Pattern recognition
Articles
medicine.disease
030104 developmental biology
Oncology
030220 oncology & carcinogenesis
Artificial intelligence
business
Subjects
Details
- ISSN :
- 17921074
- Volume :
- 20
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- Oncology letters
- Accession number :
- edsair.doi.dedup.....8d6cf6e8f4be66ee1da49511db8b6d21