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View all- Duan CChen SLi X(2024)Prototypes Contrastive Learning Empowered Intelligent Diagnosis for Skin LesionIEEE Internet of Things Journal10.1109/JIOT.2024.343508211:21(35329-35340)Online publication date: 1-Nov-2024
Skin Lesion Intelligent Diagnosis in Edge Computing Networks: An FCL Approach
Authors: 
Yanhang Shi, Xue Li, Siguang ChenAuthors Info & Claims
Article No.: 69, Pages 1 - 22
Abstract
In recent years, automatic skin lesion diagnosis methods based on artificial intelligence have achieved great success. However, the lack of labeled data, visual similarity between skin diseases, and restriction on private data sharing remain the major challenges in skin lesion diagnosis. In this article, first, we propose a federated contrastive learning framework to break down data silos and enhance the generalizability of diagnostic model to unseen data. Subsequently, by combining data features from different participated nodes, the proposed framework can improve the performance of contrastive training. To extract discriminative features during on-device training, we propose a contrastive learning based intelligent skin lesion diagnosis scheme in edge computing networks. Specifically, a contrastive learning based dual encoder network is designed to overcome training sample scarcity by fully leveraging unlabeled samples for performance improvement. Meanwhile, we devise a maximum mean discrepancy based supervised contrastive loss function, which can efficiently explore complex intra-class and inter-class variances of samples. Finally, the diagnosis simulations demonstrate that compared with existing methods, our proposed scheme can achieve superior accuracy in both on-device training and distributed training scenarios.
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- Skin Lesion Intelligent Diagnosis in Edge Computing Networks: An FCL Approach
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August 2023
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Association for Computing Machinery
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Publication History
Published: 15 June 2023
Online AM: 01 May 2023
Accepted: 24 April 2023
Revised: 28 February 2023
Received: 23 September 2022
Published in TIST Volume 14, Issue 4
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- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Jiangsu Planned Projects for Postdoctoral Research Funds
- 333 High-Level Talents Training Project of Jiangsu Province, the 1311 Talents Plan of NJUPT
- Postgraduate Research and Innovation Project of Jiangsu Province
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View all- Duan CChen SLi X(2024)Prototypes Contrastive Learning Empowered Intelligent Diagnosis for Skin LesionIEEE Internet of Things Journal10.1109/JIOT.2024.343508211:21(35329-35340)Online publication date: 1-Nov-2024
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