research-article

Skin Lesion Intelligent Diagnosis in Edge Computing Networks: An FCL Approach

Authors:

Siguang ChenAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 4

Article No.: 69, Pages 1 - 22

https://doi.org/10.1145/3595186

Published: 15 June 2023 Publication History

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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Cited By

Xu YKhan TSong YMeijering E(2025)Edge deep learning in computer vision and medical diagnostics: a comprehensive surveyArtificial Intelligence Review10.1007/s10462-024-11033-558:3Online publication date: 17-Jan-2025
https://doi.org/10.1007/s10462-024-11033-5
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
https://doi.org/10.1109/JIOT.2024.3435082

Index Terms

Skin Lesion Intelligent Diagnosis in Edge Computing Networks: An FCL Approach
1. Human-centered computing
2. Information systems
  1. Information systems applications
    1. Multimedia information systems

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 4

August 2023

481 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3596215

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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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Cited By

Xu YKhan TSong YMeijering E(2025)Edge deep learning in computer vision and medical diagnostics: a comprehensive surveyArtificial Intelligence Review10.1007/s10462-024-11033-558:3Online publication date: 17-Jan-2025
https://doi.org/10.1007/s10462-024-11033-5
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
https://doi.org/10.1109/JIOT.2024.3435082

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