Secure, privacy-preserving and federated machine learning in medical imaging

By Georgios Kaissis, Marcus R. Makowski, D. Rückert, Rickmer F. Braren. 2020

1,361 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Privacy in Deep Learning: A Survey (2020, background): “, has resulted in their deployment in numerous production systems [1, 2, 3, 4].”
Federated learning and differential privacy for medical image analysis (2021, methodology): “The steps involved in creations of a mosaic are: (1) removal of non-tissue regions using colour thresholding; (2) grouping the remaining tissue-containing patches into a pre-set number of categories through a clustering algorithm; and (3) randomly selecting a portion of all clustered patches (e.”
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space (2021, background): “Data collaboration across multiple medical institutions is increasingly desired to build accurate and robust data-driven deep networks for medical image segmentation [7, 18, 50].”
A Comprehensive Survey of Privacy-preserving Federated Learning (2021, methodology): “Kaissis et al. [84] investigated the use of FL and several privacy-preserving techniques in medical imaging.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, background): “Further, security and privacy are also considered in [117] for an FL-based medical imaging processing architecture.”
Surgical data science – from concepts toward clinical translation (2020, background): “Recent advances in federated learning could reduce security and privacy concerns since they rely on sharing machine learning models rather than the data itself (Kaissis et al., 2020) (see Section 3).”
Decentralized Federated Learning: A Survey and Perspective (2023, neutral): “…its excellent capabilities in various areas, including intelligent transportation, Internet of Things (IoT), healthcare, manufacturing, agriculture, energy, remote sensing, and more [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28].”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “Speciﬁcally, Kaissis et al. in [315] presented a FL approach for medical imaging to preserve privacy and avoid potential attacks against the datasets or learning algorithms.”
Federated Learning on Non-IID Data Silos: An Experimental Study (2021, background): “It has attracted many research interests [7, 22, 31, 36, 39, 45, 64] and been widely used in practice [3, 20, 30].”
Towards Personalized Federated Learning (2021, background): “This is especially relevant in the healthcare industry where medical data are highly-sensitive and they are often collected and reside across different healthcare institutions [1]– [4].”
Model-Contrastive Federated Learning (2021, methodology): “Moreover, it has been applied in many applications such as medical imaging [21, 23], object detection [32], and landmark classification [15].”
Generalizing to Unseen Domains: A Survey on Domain Generalization (2021, background): “On the other hand, decentralized training is another possible solution [227].”
Multimodal biomedical AI (2022, background): “Given the challenges, multiple technical solutions have been proposed and explored to ensure security and privacy while training multimodal AI models, including differential privacy, federated learning, homomorphic encryption and swarm learning 165,166 .”
Federated learning for predicting clinical outcomes in patients with COVID-19 (2021, methodology): “While differential privacy algorithms 36,48,49 show good protection, they may weaken the model’s performance.”