AI Medical Compendium Topic

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Federated Learning

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Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives.

Ophthalmology. Glaucoma
CLINICAL RELEVANCE: Glaucoma is a complex eye condition with varied morphological and clinical presentations, making diagnosis and management challenging. The lack of a consensus definition for glaucoma or glaucomatous optic neuropathy further compli...

Federated Learning for Predicting Postoperative Remission of Patients with Acromegaly: A Multicentered Study.

World neurosurgery
BACKGROUND: Decentralized federated learning (DFL) may serve as a useful framework for machine learning (ML) tasks in multicentered studies, maximizing the use of clinical data without data sharing. We aim to propose the first workflow of DFL for ML ...

Multi-Modal Federated Learning for Cancer Staging Over Non-IID Datasets With Unbalanced Modalities.

IEEE transactions on medical imaging
The use of machine learning (ML) for cancer staging through medical image analysis has gained substantial interest across medical disciplines. When accompanied by the innovative federated learning (FL) framework, ML techniques can further overcome pr...

Generalizable Reconstruction for Accelerating MR Imaging via Federated Learning With Neural Architecture Search.

IEEE transactions on medical imaging
Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstruction model. While a centralized training model is effective in mitigating...

Communication-Efficient Hybrid Federated Learning for E-Health With Horizontal and Vertical Data Partitioning.

IEEE transactions on neural networks and learning systems
Electronic healthcare (e-health) allows smart devices and medical institutions to collaboratively collect patients' data, which is trained by artificial intelligence (AI) technologies to help doctors make diagnosis. By allowing multiple devices to tr...

Hypernetwork-Based Physics-Driven Personalized Federated Learning for CT Imaging.

IEEE transactions on neural networks and learning systems
In clinical practice, computed tomography (CT) is an important noninvasive inspection technology to provide patients' anatomical information. However, its potential radiation risk is an unavoidable problem that raises people's concerns. Recently, dee...

Toward Integrating Federated Learning With Split Learning via Spatio-Temporal Graph Framework for Brain Disease Prediction.

IEEE transactions on medical imaging
Functional Magnetic Resonance Imaging (fMRI) is used for extracting blood oxygen signals from brain regions to map brain functional connectivity for brain disease prediction. Despite its effectiveness, fMRI has not been widely used: on the one hand, ...

Privacy-Preserving Technology Using Federated Learning and Blockchain in Protecting against Adversarial Attacks for Retinal Imaging.

Ophthalmology
PURPOSE: Collaboration provides valuable data for robust artificial intelligence (AI) model development. Federated learning (FL) is a privacy-enhancing technology that allows collaboration while respecting privacy via the development of models withou...