Encrypted federated learning for secure decentralized collaboration in cancer image analysis

Truhn, Daniel; Arasteh, Soroosh Tayebi; Saldanha, Oliver Lester; Mueller-Franzes, Gustav; Khader, Firas; Quirke, Philip; West, Nicholas P.; Gray, Richard; Hutchins, Gordon; James, Jacqueline; Loughrey, Maurice B.; Salto–Tellez, Manuel; Brenner, Hermann; Brobeil, Alexander; Yuan, Tanwei; Chang‐Claude, Jenny; Hoffmeister, Michael; Foersch, Sebastian; Han, Tianyu; Keil, Sebastian; Schulze-Hagen, Maximilian; Isfort, Peter; Bruners, Philipp; Kaissis, Georgios; Kühl, Christiane; Nebelung, Sven; Kather, Jakob Nikolas

doi:10.1101/2022.07.28.22277288

Cited by 4 publications

(4 citation statements)

References 48 publications

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“…As WSI deals with large amounts of sensitive patient information, strong data privacy policies, encryption, and access control are indispensable. Differential privacy methods such as encryption and federated learning protect individual privacy while allowing accurate data analysis (87–89).…”

Section: Discussionmentioning

confidence: 99%

Digital Pathology, Deep Learning, and Cancer: A Narrative Review

Williams,

Graifman,

Hussain

et al. 2024

Preprint

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Background and Objective: Cancer is a leading cause of morbidity and mortality worldwide. The emergence of digital pathology and deep learning technologies signifies a transformative era in healthcare. These technologies can enhance cancer detection, streamline operations, and bolster patient care. A substantial gap exists between the development phase of deep learning models in controlled laboratory environments and their translations into clinical practice. This narrative review evaluates the current landscape of deep learning and digital pathology, analyzing the factors influencing model development and implementation into clinical practice. Methods: We searched multiple databases, including Web of Science, Arxiv, MedRxiv, BioRxiv, Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, and Cochrane, targeting articles on whole slide imaging and deep learning published from 2014 and 2023. Out of 776 articles identified based on inclusion criteria, we selected 36 papers for the analysis. Key Content and Findings: Most articles in this review focus on the in-laboratory phase of deep learning model development, a critical stage in the deep learning lifecycle. Challenges arise during model development and their integration into clinical practice. Notably, lab performance metrics may not always match real-world clinical outcomes. As technology advances and regulations evolve, we expect more clinical trials to bridge this performance gap and validate deep learning models' effectiveness in clinical care. High clinical accuracy is vital for informed decision-making throughout a patient's cancer care. Conclusions: Deep learning technology can enhance cancer detection, clinical workflows, and patient care. Challenges may arise during model development. The deep learning lifecycle involves data preprocessing, model development, and clinical implementation. Achieving health equity requires including diverse patient groups and eliminating bias during implementation. While model development is integral, most articles focus on the pre-deployment phase. Future longitudinal studies are crucial for validating models in real-world settings post-deployment. A collaborative approach among computational pathologists, technologists, industry, and healthcare providers is essential for driving adoption in clinical settings. Keywords: Artificial Intelligence, Deep Learning, Digital Pathology, Computational Pathology, Cancer

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Section: Discussionmentioning

confidence: 99%

Digital Pathology, Deep Learning, and Cancer: A Narrative Review

Williams,

Graifman,

Hussain

et al. 2024

Preprint

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“…In addition, Jupyter-Notebook-based tools, such as [24], also help simplify the FL setup and enable its deployment of a cross-country federated environment in only a few minutes. Daniel Truhn in [25] employed homomorphic encryption to protect the model's performance while training by encrypting the weight updates before sharing them with the central server. Firas Khader in [26] presented a technique of "learnable synergy", where the model only chooses pertinent interactions between data modalities and maintains an"internal memory" of key information.…”

Section: Federated Learning (Fl)mentioning

confidence: 99%

WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval

Tabatabaei,

Wang,

Colomer

et al. 2023

Bioengineering

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The paper proposes a federated content-based medical image retrieval (FedCBMIR) tool that utilizes federated learning (FL) to address the challenges of acquiring a diverse medical data set for training CBMIR models. CBMIR is a tool to find the most similar cases in the data set to assist pathologists. Training such a tool necessitates a pool of whole-slide images (WSIs) to train the feature extractor (FE) to extract an optimal embedding vector. The strict regulations surrounding data sharing in hospitals makes it difficult to collect a rich data set. FedCBMIR distributes an unsupervised FE to collaborative centers for training without sharing the data set, resulting in shorter training times and higher performance. FedCBMIR was evaluated by mimicking two experiments, including two clients with two different breast cancer data sets, namely BreaKHis and Camelyon17 (CAM17), and four clients with the BreaKHis data set at four different magnifications. FedCBMIR increases the F1 score (F1S) of each client from 96% to 98.1% in CAM17 and from 95% to 98.4% in BreaKHis, with 11.44 fewer hours in training time. FedCBMIR provides 98%, 96%, 94%, and 97% F1S in the BreaKHis experiment with a generalized model and accomplishes this in 25.53 fewer hours of training.

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“…They only received an aggregate network without any information on the contributions of other participating institutions to the global network. Following the convergence of the training phase for the global classification network, each institution had the opportunity to retain a copy of the global network for local utilization on their respective test data 12,14 .…”

Section: Federated Learningmentioning

confidence: 99%

“…and performant, i.e., generalizing AI models. Federated learning (FL) [9][10][11][12][13][14] , particularly the Federated Averaging (FedAvg) 11 algorithm, presents a promising solution. This approach allows AI models to be collaboratively trained across various sites without data exchange, thereby preserving data privacy.…”

mentioning

confidence: 99%

Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning

Tayebi Arasteh,

Kuhl,

Saehn

et al. 2023

Sci Rep

Self Cite

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Developing robust artificial intelligence (AI) models that generalize well to unseen datasets is challenging and usually requires large and variable datasets, preferably from multiple institutions. In federated learning (FL), a model is trained collaboratively at numerous sites that hold local datasets without exchanging them. So far, the impact of training strategy, i.e., local versus collaborative, on the diagnostic on-domain and off-domain performance of AI models interpreting chest radiographs has not been assessed. Consequently, using 610,000 chest radiographs from five institutions across the globe, we assessed diagnostic performance as a function of training strategy (i.e., local vs. collaborative), network architecture (i.e., convolutional vs. transformer-based), single versus cross-institutional performance (i.e., on-domain vs. off-domain), imaging finding (i.e., cardiomegaly, pleural effusion, pneumonia, atelectasis, consolidation, pneumothorax, and no abnormality), dataset size (i.e., from n = 18,000 to 213,921 radiographs), and dataset diversity. Large datasets not only showed minimal performance gains with FL but, in some instances, even exhibited decreases. In contrast, smaller datasets revealed marked improvements. Thus, on-domain performance was mainly driven by training data size. However, off-domain performance leaned more on training diversity. When trained collaboratively across diverse external institutions, AI models consistently surpassed models trained locally for off-domain tasks, emphasizing FL’s potential in leveraging data diversity. In conclusion, FL can bolster diagnostic privacy, reproducibility, and off-domain reliability of AI models and, potentially, optimize healthcare outcomes.

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Encrypted federated learning for secure decentralized collaboration in cancer image analysis

Cited by 4 publications

References 48 publications

Digital Pathology, Deep Learning, and Cancer: A Narrative Review

Digital Pathology, Deep Learning, and Cancer: A Narrative Review

WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval

Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning

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