Adaptive Multimodal Fusion in Vertical Federated Learning for Decentralized Glaucoma Screening.

IF 2.8 3区 医学 Q3 NEUROSCIENCES
Ayesha Jabbar, Jianjun Huang, Muhammad Kashif Jabbar, Asad Ali
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引用次数: 0

Abstract

Background/Objectives: Early and accurate detection of glaucoma is vital for preventing irreversible vision loss, yet traditional diagnostic approaches relying solely on unimodal retinal imaging are limited by data sparsity and constrained context. Furthermore, real-world clinical data are often fragmented across institutions under strict privacy regulations, posing significant challenges for centralized machine learning methods. Methods: To address these barriers, this study proposes a novel Quality Aware Vertical Federated Learning (QAVFL) framework for decentralized multimodal glaucoma detection. The proposed system dynamically integrates clinical text, retinal fundus images, and biomedical signal data through modality-specific encoders, followed by a Fusion Attention Module (FAM) that adaptively weighs the reliability and contribution of each modality. Unlike conventional early fusion or horizontal federated learning methods, QAVFL operates in vertically partitioned environments and employs secure aggregation mechanisms incorporating homomorphic encryption and differential privacy to preserve patient confidentiality. Results: Extensive experiments conducted under heterogeneous non-IID settings demonstrate that QAVFL achieves an accuracy of 98.6%, a recall of 98.6%, an F1-score of 97.0%, and an AUC of 0.992, outperforming unimodal and early fusion baselines with statistically significant improvements (p < 0.01). Conclusions: The findings validate the effectiveness of dynamic multimodal fusion under privacy-preserving decentralized learning and highlight the scalability and clinical applicability of QAVFL for robust glaucoma screening across fragmented healthcare environments.

分散青光眼筛查中垂直联合学习的自适应多模态融合。
背景/目的:青光眼的早期和准确检测对于预防不可逆的视力丧失至关重要,然而传统的仅依赖单峰视网膜成像的诊断方法受到数据稀疏性和环境约束的限制。此外,现实世界的临床数据往往在严格的隐私法规下分散在各个机构中,这对集中式机器学习方法构成了重大挑战。方法:为了解决这些障碍,本研究提出了一种新的质量感知垂直联邦学习(QAVFL)框架,用于分散的多模式青光眼检测。该系统通过模式特定的编码器动态集成临床文本、视网膜眼底图像和生物医学信号数据,然后是一个融合注意模块(FAM),该模块自适应地权衡每个模式的可靠性和贡献。与传统的早期融合或水平联邦学习方法不同,QAVFL在垂直分区的环境中运行,并采用结合同态加密和差分隐私的安全聚合机制来保护患者的机密性。结果:在异质非iid设置下进行的大量实验表明,QAVFL的准确率为98.6%,召回率为98.6%,f1评分为97.0%,AUC为0.992,优于单峰和早期融合基线,具有统计学意义的改善(p < 0.01)。结论:研究结果验证了隐私保护分散学习下动态多模态融合的有效性,并强调了QAVFL在分散医疗环境中健壮青光眼筛查的可扩展性和临床适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Brain Sciences
Brain Sciences Neuroscience-General Neuroscience
CiteScore
4.80
自引率
9.10%
发文量
1472
审稿时长
18.71 days
期刊介绍: Brain Sciences (ISSN 2076-3425) is a peer-reviewed scientific journal that publishes original articles, critical reviews, research notes and short communications in the areas of cognitive neuroscience, developmental neuroscience, molecular and cellular neuroscience, neural engineering, neuroimaging, neurolinguistics, neuropathy, systems neuroscience, and theoretical and computational neuroscience. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files or software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.
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