Federated Learning for Site Aware Chest Radiograph Screening

Chakravarty, Arunava; Kar, Avik; Sethuraman, Ramanathan; Sheet, Debdoot

doi:10.1109/isbi48211.2021.9433876

Cited by 8 publications

(14 citation statements)

References 8 publications

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“…Linardos et al [26] considered FL for Diagnosing Hypertrophic Cardiomyopathy (HCM), whether the subjects are suffering from HMC or normal. In addition to that, a multilabel cardiac diseases classification has been proposed by Chakravarty et al [23], where 14 classes were examined. The other application includes Autism Spectrum Disorders (ASD) detection.…”

Section: A Overviewmentioning

confidence: 99%

“…Likewise, as we know smoking is dangerous for health, which particularly affects our lungs and a key reason for lung cancer. According to our investigation, six articles [18], [23], [25], [27], [29], [32] have used chest X-ray images out of 17. The X-ray datasets considered in the articles are Cohen JP, TB x-ray, CheXpert, Mendeley data, COVIDx, Chest X-ray (CXR), and COVID 2019 dataset.…”

Section: Rq3 What Type Of Dataset Used?mentioning

confidence: 99%

“…Since larger dataset belongs to better potentiality of inside analysis, literally 153 data samples are not technically sound. Next, within the 10,000 sample range, CXR image) found under our investigation was considered by Chakravarty et al [23].…”

Section: Rq3 What Type Of Dataset Used?mentioning

confidence: 99%

“…Low: The rest three articles performed multi-class classification, where Chakravarty et al [23] 14, Połap et al [17] seven, and Li et al [20] have considered four classes, their acquired performances are AUC 80%, accuracy 70% and 76% respectively.…”

Section: E Experimentalmentioning

confidence: 99%

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A Systematic Review on Federated Learning in Medical Image Analysis

Sohan

Basalamah

2023

IEEE Access

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Federated Learning (FL) obtained a lot of attention to the academic and industrial stakeholders from the beginning of its invention. The eye-catching feature of FL is handling data in a decentralized manner which creates a privacy preserving environment in Artificial Intelligence (AI) applications. As we know medical data includes marginal private information of patients which demands excessive data protection from disclosure to unexpected destinations. In this paper, we performed a Systematic Literature Review (SLR) of published research articles on FL based medical image analysis. Firstly, we have collected articles from different databases followed by PRISMA guidelines, then synthesized data from the selected articles, and finally we provided a comprehensive overview on the topic. In order to do that we extracted core information associated with the implementation of FL in medical imaging from the articles. In our findings we briefly presented characteristics of federated data and models, performance achieved by the models and exclusively results comparison with traditional ML models. In addition, we discussed the open issues and challenges of implementing FL and mentioned our recommendations for future direction of this particular research field. We believe this SLR has successfully summarized the state-of-the-art FL methods for medical image analysis using deep learning.INDEX TERMS Federated learning, machine learning, medical image analysis, data privacy, systematic literature review.

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Section: A Overviewmentioning

confidence: 99%

Section: Rq3 What Type Of Dataset Used?mentioning

confidence: 99%

Section: Rq3 What Type Of Dataset Used?mentioning

confidence: 99%

Section: E Experimentalmentioning

confidence: 99%

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A Systematic Review on Federated Learning in Medical Image Analysis

Sohan

Basalamah

2023

IEEE Access

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“…The feasibility of federated learning on chest X-rays has previously been benchmarked for both the CheXpert [ 24 ] and the Mendeley [ 25 ] dataset. Chakravarty et al [ 26 ] enhance a ResNet18 architecture with a graph neural network for federated learning on CheXpert data with site-specific data distributions. Nath et al [ 27 ] deploy a DenseNet121 model for a real-world, physically distributed implementation of federated learning on CheXpert.…”

Section: Related Workmentioning

confidence: 99%

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data

Ziegler

Pfitzner

Schulz

et al. 2022

Sensors

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Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.94 for ε=6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.

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Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

Shiri

Sadr

Akhavan

et al. 2022

Eur J Nucl Med Mol Imaging

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Purpose Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images. Methods Non-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) 18F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center). Results In terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21–14.81%) and FL-PL (CI:11.82–13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32–12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34–26.10%). Furthermore, the Mann–Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R2 = 0.94), FL-SQ (R2 = 0.93), and FL-PL (R2 = 0.92), while CB model achieved a far lower coefficient of determination (R2 = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed. Conclusion Deep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers.

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Federated Learning for Site Aware Chest Radiograph Screening

Cited by 8 publications

References 8 publications

A Systematic Review on Federated Learning in Medical Image Analysis

A Systematic Review on Federated Learning in Medical Image Analysis

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data

Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

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