Yongyang Bao1, Jiayi Zhang2, Xingyu Zhao2, Henghua Zhou1, Ying Chen1, Junming Jian2, Tianlei Shi2, Xin Gao2,3. 1. Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China. 2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China. 3. Jinan Guoke Medical Engineering, Technology Development Co., Ltd, Jinan, Shandong, China.
Abstract
BACKGROUND: Erroneous diagnoses of melanocytic lesions (benign, atypical, and malignant types) result in inappropriate surgical treatment plans. OBJECTIVE: To propose a deep learning (DL)-based fully automated diagnostic method using whole slide images (WSIs) for melanocytic lesions. METHODS: The method consisted of patch prediction using a DL model and patient diagnosis using an aggregation module. The method was developed with 745 WSIs and evaluated using internal and external testing sets comprising 182 WSIs and 54 WSIs, respectively. The results were compared with those of the classification by one junior and two senior pathologists. Furthermore, we compared the performance of the three pathologists in the classification of melanocytic lesions with and without the assistance of our method. RESULTS: The method achieved an accuracy of 0.963 and 0.930 on the internal and external testing sets, respectively, which was significantly higher than that of the junior pathologist (0.419 and 0.535). With assistance from the method, all three pathologists achieved higher accuracy on the internal and external testing sets; the accuracy of the junior pathologist increased by 39.0% and 30.2%, respectively (p < .05). CONCLUSION: This generalizable method can accurately classify melanocytic lesions and effectively improve the diagnostic accuracy of pathologists.
BACKGROUND: Erroneous diagnoses of melanocytic lesions (benign, atypical, and malignant types) result in inappropriate surgical treatment plans. OBJECTIVE: To propose a deep learning (DL)-based fully automated diagnostic method using whole slide images (WSIs) for melanocytic lesions. METHODS: The method consisted of patch prediction using a DL model and patient diagnosis using an aggregation module. The method was developed with 745 WSIs and evaluated using internal and external testing sets comprising 182 WSIs and 54 WSIs, respectively. The results were compared with those of the classification by one junior and two senior pathologists. Furthermore, we compared the performance of the three pathologists in the classification of melanocytic lesions with and without the assistance of our method. RESULTS: The method achieved an accuracy of 0.963 and 0.930 on the internal and external testing sets, respectively, which was significantly higher than that of the junior pathologist (0.419 and 0.535). With assistance from the method, all three pathologists achieved higher accuracy on the internal and external testing sets; the accuracy of the junior pathologist increased by 39.0% and 30.2%, respectively (p < .05). CONCLUSION: This generalizable method can accurately classify melanocytic lesions and effectively improve the diagnostic accuracy of pathologists.
Entities:
Keywords:
Melanocytic lesion; deep learning; pathological diagnosis; whole slide image