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Volume 11
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Article

Computer-Assisted Fine-Needle Aspiration Cytology of Thyroid Using Two-Stage Refined Convolutional Neural Network

by
Wensi Duan
1,†,
Lili Gao
2,†,
Juan Liu
1,*,
Cheng Li
3,
Peng Jiang
1,
Lang Wang
1,
Hua Chen
1,
Xiaorong Sun
3,
Dehua Cao
3,
Baochuan Pang
3,
Rong Li
3 and
Sai Liu
3
1
Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan 430072, China
2
Department of Pathology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
3
Landing Artificial Intelligence Center for Pathological Diagnosis, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Electronics 2022, 11(24), 4089; https://doi.org/10.3390/electronics11244089
Submission received: 4 November 2022 / Revised: 27 November 2022 / Accepted: 5 December 2022 / Published: 8 December 2022
(This article belongs to the Section Artificial Intelligence)
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Abstract

Fine-needle aspiration cytology (FNAC) is regarded as one of the most important preoperative diagnostic tests for thyroid nodules. However, the traditional diagnostic process of FNAC is time-consuming, and its accuracy is highly related to the experience of the cytopathologist. Computer-aided diagnostic (CAD) systems are rapidly evolving to provide objective diagnostic recommendations. So far, most studies have used fixed-size patches and usually hand-select patches for model training. In this study, we develop a CAD system to address these challenges. In order to be consistent with the diagnostic working mode of cytopathologists, the system is mainly composed of two task modules: the detecting module that is responsible for detecting the regions of interest (ROIs) from the whole slide image of the FNAC, and the classification module that identifies ROIs having positive lesions. The system can then output the top-k ROIs with the highest positive probabilities for the cytopathologists to review. In order to obtain the overall good performance of the system, we compared different object detection and classification models, and used a combination of the YOLOV4 and EfficientNet networks in our system.
Keywords: two-stage CAD system; fine-needle aspiration cytology (FNAC); thyroid cytopathology; deep learning; object detection two-stage CAD system; fine-needle aspiration cytology (FNAC); thyroid cytopathology; deep learning; object detection

Share and Cite

MDPI and ACS Style

Duan, W.; Gao, L.; Liu, J.; Li, C.; Jiang, P.; Wang, L.; Chen, H.; Sun, X.; Cao, D.; Pang, B.; et al. Computer-Assisted Fine-Needle Aspiration Cytology of Thyroid Using Two-Stage Refined Convolutional Neural Network. Electronics 2022, 11, 4089. https://doi.org/10.3390/electronics11244089

AMA Style

Duan W, Gao L, Liu J, Li C, Jiang P, Wang L, Chen H, Sun X, Cao D, Pang B, et al. Computer-Assisted Fine-Needle Aspiration Cytology of Thyroid Using Two-Stage Refined Convolutional Neural Network. Electronics. 2022; 11(24):4089. https://doi.org/10.3390/electronics11244089

Chicago/Turabian Style

Duan, Wensi, Lili Gao, Juan Liu, Cheng Li, Peng Jiang, Lang Wang, Hua Chen, Xiaorong Sun, Dehua Cao, Baochuan Pang, and et al. 2022. "Computer-Assisted Fine-Needle Aspiration Cytology of Thyroid Using Two-Stage Refined Convolutional Neural Network" Electronics 11, no. 24: 4089. https://doi.org/10.3390/electronics11244089

APA Style

Duan, W., Gao, L., Liu, J., Li, C., Jiang, P., Wang, L., Chen, H., Sun, X., Cao, D., Pang, B., Li, R., & Liu, S. (2022). Computer-Assisted Fine-Needle Aspiration Cytology of Thyroid Using Two-Stage Refined Convolutional Neural Network. Electronics, 11(24), 4089. https://doi.org/10.3390/electronics11244089

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