Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study.

Tao Li,Mingliang Chen,Wenjie Kang,Jie Liu,Fangfang Li,Ke Zuo,Peizhen Xie,Shuang Zhao,Rahim Khan

doi:10.1155/2021/5972962

Abstract

In traditional hospital systems, diagnosis and localization of melanoma are the critical challenges for pathological analysis, treatment instructions, and prognosis evaluation particularly in skin diseases. In literature, various studies have been reported to address these issues; however, a prominent smart diagnosis system is needed to be developed for the smart healthcare system. In this study, a deep learning-enabled diagnostic system is proposed and implemented that it has the capacity to automatically detect malignant melanoma in whole slide images (WSIs). In this system, the convolutional neural network (CNN), sophisticated statistical method, and image processing algorithms were integrated and implemented to locate benign and malignant lesions which are extremely useful in the diagnoses process of melanoma disease. To verify the exceptional performance of the proposed scheme, it is implemented in a multicenter database, which has 701 WSIs (641 WSIs from Central South University Xiangya Hospital (CSUXH) and 60 WSIs from the Cancer Genome Atlas (TCGA)). Experimental results have verified that the proposed system has achieved an area under the receiver operating characteristic curve (AUROC) of 0.971. Furthermore, the lesion area on the WSIs is represented by its degree of malignancy. These results show that the proposed system has the capacity to fully automate the diagnosis and localization problem of the melanoma in the smart healthcare systems.

Highlights

According to the Global Cancer Statistics of 2018, approximately 120,000 patients with skin cancer succumb to the disease each year, while another 1,300,000 cases are diagnosed [1]
Lesion location shows the diagnostic basis of the proposed model to predict the expected results, which is critical for pathologists to understand why the model makes its decision. e lesion areas were located using the probability heat maps method, a data visualization technique that shows the magnitude of probability as color
The convolutional neural network (CNN), sophisticated statistical method, and image processing algorithms were integrated and implemented to locate benign and malignant lesions which are extremely useful in the diagnoses process of melanoma disease

Summary

Introduction

According to the Global Cancer Statistics of 2018, approximately 120,000 patients with skin cancer succumb to the disease each year, while another 1,300,000 cases are diagnosed [1]. Us, sophisticated and well-trained diagnosis methods are needed to be developed to minimize the death ratio. The most accurate and precise diagnosis method with maximum successful treatment ratio for melanoma cancer is the accurate diagnosis of hematoxylin and eosin- (H&E-) stained tissue slides [3]. This method is computationally expensive and time consuming. Erefore, it is urgent and critical to develop a fast, accurate, and high-precise method to assist pathologists in the diagnostic process of melanoma skin cancer. Ese studies are based on topology, statistic, or machine learning approaches to predict expected ratio of the underlined skin cancer disease. Due to the technological limitations, high performance of these studies is confined to the small handpicked dataset, which limits its clinical application. erefore, an automated

Results

Discussion

Conclusion