Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

Liu, Quande; Yang, Hongzheng; Dou, Qi; Heng, Pheng‐Ann

doi:10.48550/arxiv.2106.08600

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…This work uses different medical images to examine the efficiency of the medical image pattern analysis process. Here, two datasets, namely, the Alzheimer's Disease Neuroimaging Initiative (ADNI) [27] and the ELCAP Public Lung Image Database [28], are utilized to determine the efficiency of the system. The ADNI dataset has 100 brain MRI images utilized to extract the hippocampal regions effectively.…”

Section: Resultsmentioning

confidence: 99%

“…This process effectively identified the pathological findings of small incidence. Liu et al [28] developed federated semi-supervised learning to learn a federated model using labeled and unlabeled customer data (i.e., hospitals). The proposed learning system explicitly connected learning to labeled and unlabeled customers by aligning their relationships with an extracted disease, mitigating the lack of task knowledge for unlabeled customers, and promoting discrimination against unlabeled customers.…”

Section: Related Workmentioning

confidence: 99%

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Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

Jaber¹,

Yussof²,

Elameer³

et al. 2022

Computers, Materials &Amp; Continua

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Artificial intelligence plays an essential role in the medical and health industries. Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis. However, convolution networks examine medical images effectively; such systems require high computational complexity when recognizing the same disease-affected region. Therefore, an optimized deep convolution network is utilized for analyzing disease-affected regions in this work. Different disease-related medical images are selected and examined pixel by pixel; this analysis uses the gray wolf optimized deep learning network. This method identifies affected pixels by the gray wolf hunting process. The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis. The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule. The pattern-matching process recognizes the disease effectively, and the system's efficiency is evaluated using the MATLAB implementation process. This process ensures high accuracy of up to 99.02% to 99.37% and reduces computational complexity.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

Jaber¹,

Yussof²,

Elameer³

et al. 2022

Computers, Materials &Amp; Continua

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“…While the bulk of the papers we've reviewed so far focus purely on designing federated algorithms that can predict different aspects of cancer with high degrees of accuracy, a large sub-group of the papers in our review also aim at addressing challenges federated learning currently faces. For many papers, that challenge is either data heterogeneity [58][59][60][61][62][63][64][65], a common barrier in the medi-cal field where patients can be subject to different geographic and demographic conditions, or label deficiency [66,67], where it is not always guaranteed that clients' sites will have access to labeled data.…”

Section: Federated Learning Algorithmsmentioning

confidence: 99%

“…Addressing label deficiency, [66] introduced a new Federated Semi-Supervised Learning (FSSL) approach for skin lesion classification. Their method is inspired by knowledge distillation [68], where they model disease relationships in each client by a relation matrix calculated from the local model output, then aggregate the relation matrices from all clients to form a global one that is used locally in each round to ensure that clients will have similar disease relationships.…”

Section: Federated Learning Algorithmsmentioning

confidence: 99%

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

Chowdhury

Kassem

Padoy

et al. 2022

Lecture Notes in Computer Science

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Machine learning has revolutionized every facet of human life, while also becoming more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare, with numerous applications and intelligent systems achieving clinical level expertise. However, building robust and generalizable systems relies on training algorithms in a centralized fashion using large, heterogeneous datasets. In medicine, these datasets are time consuming to annotate and difficult to collect centrally due to privacy concerns. Recently, Federated Learning has been proposed as a distributed learning technique to alleviate many of these privacy concerns by providing a decentralized training paradigm for models using large, distributed data. This new approach has become the defacto way of building machine learning models in multiple industries (e.g. edge computing, smartphones). Due to its strong potential, Federated Learning is also becoming a popular training method in healthcare, where patient privacy is of paramount concern. In this paper we performed an extensive literature review to identify state-of-the-art Federated Learning applications for cancer research and clinical oncology analysis. Our objective is to provide readers with an overview of the evolving Federated Learning landscape, with a focus on applications and algorithms in oncology space. Moreover, we hope that this review will help readers to identify potential needs and future directions for research and development.

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Section: Federated Learning Algorithmsmentioning

confidence: 99%

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

2020

Lecture Notes in Computer Science

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material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

Cited by 6 publications

References 24 publications

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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