AIMC Journal:
IEEE transactions on medical imaging

Showing 181 to 190 of 687 articles

Proportionally Fair Hospital Collaborations in Federated Learning of Histopathology Images.

IEEE transactions on medical imaging Jun 30, 2023
Medical centers and healthcare providers have concerns and hence restrictions around sharing data with external collaborators. Federated learning, as a privacy-preserving method, involves learning a site-independent model without having direct access...
Humans Hospitals Supervised Machine Learning Pathology
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FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation.

IEEE transactions on medical imaging Jun 30, 2023
The purpose of federated learning is to enable multiple clients to jointly train a machine learning model without sharing data. However, the existing methods for training an image segmentation model have been based on an unrealistic assumption that t...
Humans Machine Learning Supervised Machine Learning
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Federated Partially Supervised Learning With Limited Decentralized Medical Images.

IEEE transactions on medical imaging Jun 30, 2023
Data government has played an instrumental role in securing the privacy-critical infrastructure in the medical domain and has led to an increased need of federated learning (FL). While decentralization can limit the effectiveness of standard supervis...
Humans Diagnostic Imaging Supervised Machine Learning
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A Dataset Auditing Method for Collaboratively Trained Machine Learning Models.

IEEE transactions on medical imaging Jun 30, 2023
Dataset auditing for machine learning (ML) models is a method to evaluate if a given dataset is used in training a model. In a Federated Learning setting where multiple institutions collaboratively train a model with their decentralized private datas...
Machine Learning Datasets as Topic
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Federated Cycling (FedCy): Semi-Supervised Federated Learning of Surgical Phases.

IEEE transactions on medical imaging Jun 30, 2023
Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on diverse datasets from multiple m...
Supervised Machine Learning Video Recording Surgical Procedures, Operative
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A Federated Learning System for Histopathology Image Analysis With an Orchestral Stain-Normalization GAN.

IEEE transactions on medical imaging Jun 30, 2023
Currently, data-driven based machine learning is considered one of the best choices in clinical pathology analysis, and its success is subject to the sufficiency of digitized slides, particularly those with deep annotations. Although centralized trai...
Humans Machine Learning Image Processing, Computer-Assisted
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Federated Learning of Generative Image Priors for MRI Reconstruction.

IEEE transactions on medical imaging Jun 30, 2023
Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has recently been introduced to address privacy concerns by enabling ...
Humans Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Multi-Task Distributed Learning Using Vision Transformer With Random Patch Permutation.

IEEE transactions on medical imaging Jun 30, 2023
The widespread application of artificial intelligence in health research is currently hampered by limitations in data availability. Distributed learning methods such as federated learning (FL) and split learning (SL) are introduced to solve this prob...
Humans Artificial Intelligence
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Federated Learning With Privacy-Preserving Ensemble Attention Distillation.

IEEE transactions on medical imaging Jun 30, 2023
Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. This is particularly relevant for clinical applications since patient data are usually ...
Humans Machine Learning Privacy
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FedNI: Federated Graph Learning With Network Inpainting for Population-Based Disease Prediction.

IEEE transactions on medical imaging Jun 30, 2023
Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis. Specifically, in medical applications, GCNs can be used for disease prediction on a population graph, where graph nodes represent individuals and edges represent individua...
Humans Neural Networks, Computer Machine Learning Neuroimaging
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