Self-supervised learning mechanism for identification of eyelid malignant melanoma in pathologic slides with limited annotation

Linyan Wang; Zijing Jiang; An Shao; Zhengyun Liu; Renshu Gu; Ruiquan Ge; Gangyong Jia; Yaqi Wang; Juan Ye

doi:10.3389/fmed.2022.976467

Self-supervised learning mechanism for identification of eyelid malignant melanoma in pathologic slides with limited annotation

Front Med (Lausanne). 2022 Sep 27:9:976467. doi: 10.3389/fmed.2022.976467. eCollection 2022.

Authors

Linyan Wang¹, Zijing Jiang², An Shao¹, Zhengyun Liu³, Renshu Gu², Ruiquan Ge², Gangyong Jia², Yaqi Wang⁴, Juan Ye¹

Affiliations

¹ Department of Ophthalmology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
² School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.
³ Department of Pathology, Lishui Municipal Central Hospital, Lishui, China.
⁴ College of Media Engineering, The Communication University of Zhejiang, Hangzhou, China.

Abstract

Purpose: The lack of finely annotated pathologic data has limited the application of deep learning systems (DLS) to the automated interpretation of pathologic slides. Therefore, this study develops a robust self-supervised learning (SSL) pathology diagnostic system to automatically detect malignant melanoma (MM) in the eyelid with limited annotation.

Design: Development of a self-supervised diagnosis pipeline based on a public dataset, then refined and tested on a private, real-world clinical dataset.

Subjects: A. Patchcamelyon (PCam)-a publicly accessible dataset for the classification task of patch-level histopathologic images. B. The Second Affiliated Hospital, Zhejiang University School of Medicine (ZJU-2) dataset - 524,307 patches (small sections cut from pathologic slide images) from 192 H&E-stained whole-slide-images (WSIs); only 72 WSIs were labeled by pathologists.

Methods: Patchcamelyon was used to select a convolutional neural network (CNN) as the backbone for our SSL-based model. This model was further developed in the ZJU-2 dataset for patch-level classification with both labeled and unlabeled images to test its diagnosis ability. Then the algorithm retrieved information based on patch-level prediction to generate WSI-level classification results using random forest. A heatmap was computed for visualizing the decision-making process.

Main outcome measures: The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the performance of the algorithm in identifying MM.

Results: ResNet50 was selected as the backbone of the SSL-based model using the PCam dataset. This algorithm then achieved an AUC of 0.981 with an accuracy, sensitivity, and specificity of 90.9, 85.2, and 96.3% for the patch-level classification of the ZJU-2 dataset. For WSI-level diagnosis, the AUC, accuracy, sensitivity, and specificity were 0.974, 93.8%, 75.0%, and 100%, separately. For every WSI, a heatmap was generated based on the malignancy probability.

Conclusion: Our diagnostic system, which is based on SSL and trained with a dataset of limited annotation, can automatically identify MM in pathologic slides and highlight MM areas in WSIs by a probabilistic heatmap. In addition, this labor-saving and cost-efficient model has the potential to be refined to help diagnose other ophthalmic and non-ophthalmic malignancies.

Keywords: artificial intelligence - assisted bioinformatic analysis; melanoma; pathology; self-supervised deep learning; tumor diagnosis.