Deep learning-based fully automated intelligent and precise diagnosis for melanocytic lesions.
10.7507/1001-5515.202203080
- Author:
Tianlei SHI
1
;
Jiayi ZHANG
1
;
Yongyang BAO
2
;
Xin GAO
3
Author Information
1. School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, P. R. China.
2. Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200011, P. R. China.
3. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China.
- Publication Type:Journal Article
- Keywords:
Color normalization;
Deep learning;
Intelligent and precise diagnosis;
Melanocytic lesions;
Whole slide images
- MeSH:
Humans;
Deep Learning;
Melanoma/pathology*;
Diagnosis, Computer-Assisted;
Neural Networks, Computer;
Skin/pathology*
- From:
Journal of Biomedical Engineering
2022;39(5):919-927
- CountryChina
- Language:Chinese
-
Abstract:
Melanocytic lesions occur on the surface of the skin, in which the malignant type is melanoma with a high fatality rate, seriously endangering human health. The histopathological analysis is the gold standard for diagnosis of melanocytic lesions. In this study, a fully automated intelligent diagnosis method based on deep learning was proposed to classify the pathological whole slide images (WSI) of melanocytic lesions. Firstly, the color normalization based on CycleGAN neural network was performed on multi-center pathological WSI; Secondly, ResNet-152 neural network-based deep convolutional network prediction model was built using 745 WSI; Then, a decision fusion model was cascaded, which calculates the average prediction probability of each WSI; Finally, the diagnostic performance of the proposed method was verified by internal and external test sets containing 182 and 54 WSI, respectively. Experimental results showed that the overall diagnostic accuracy of the proposed method reached 94.12% in the internal test set and exceeded 90% in the external test set. Furthermore, the color normalization method adopted was superior to the traditional color statistics-based and staining separation-based methods in terms of structure preservation and artifact suppression. The results demonstrate that the proposed method can achieve high precision and strong robustness in pathological WSI classification of melanocytic lesions, which has the potential in promoting the clinical application of computer-aided pathological diagnosis.
