4.7 Article

Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Hardware & Architecture

Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

Adnan Qayyum et al.

Summary: Despite improvements in cloud-based healthcare applications, their limited ability to meet strict security, privacy, and quality of service requirements has hindered their adoption. The use of edge computing and distributed machine learning techniques like federated learning has gained popularity as a solution. This paper explores the potential of using edge computing for intelligent processing of clinical data in medicine, specifically for automatic COVID-19 diagnosis using clustered federated learning. Promising results were obtained on benchmark datasets, achieving significant improvements in F1-Scores compared to centralized models.

IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY (2022)

Article Computer Science, Interdisciplinary Applications

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

Victor M. Campello et al.

Summary: The emergence of deep learning has advanced cardiac magnetic resonance segmentation, yet current models lack generalizability. A recent competition emphasized the importance of data augmentation in training deep learning models and provided new data resources for future research.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

Article Computer Science, Information Systems

Federated learning improves site performance in multicenter deep learning without data sharing

Karthik Sarma et al.

Summary: The study demonstrates the effectiveness of federated learning in enabling multi-institutional training without centralizing or sharing underlying physical data. Results show that the federated learning model outperformed single-institution models in terms of performance and generalizability. This approach successfully showcases the ability to accelerate model development while maintaining privacy.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

Oliver Diaz et al.

Summary: The vast amount of data generated by medical imaging systems today has led professionals to explore novel technologies such as artificial intelligence to efficiently handle and utilize the data. Proper preparation of medical images is crucial for the development of reliable AI algorithms, involving steps like image acquisition, de-identification, data curation, storage, and annotation. Open access tools and medical image repositories play important roles in these processes, with future work in this area focusing on further advancements.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2021)

Article Computer Science, Information Systems

Dynamic-Fusion-Based Federated Learning for COVID-19 Detection

Weishan Zhang et al.

Summary: This study introduces a dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. By dynamically selecting participating clients and scheduling model fusion, communication efficiency and model performance are improved.

IEEE INTERNET OF THINGS JOURNAL (2021)

Article Computer Science, Artificial Intelligence

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

Dong Yang et al.

Summary: This study explores the use of federated and semi-supervised learning techniques to address the variability in data and annotations in COVID-19 detection. Through experiments on a multinational database, it is found that federated learning protects data privacy and semi-supervised learning helps reduce annotation burden. This novel approach shows promising results in transfer learning.

MEDICAL IMAGE ANALYSIS (2021)

Review Engineering, Electrical & Electronic

A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

S. Kevin Zhou et al.

Summary: Deep learning has been applied in medical imaging tasks with remarkable success, however facing unique challenges. This survey article presents traits of medical imaging, clinical needs, technical challenges, and how emerging trends in DL are addressing these issues. Case studies include digital pathology and various imaging modalities in clinical practice.

PROCEEDINGS OF THE IEEE (2021)

Article Computer Science, Interdisciplinary Applications

TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning

Fernando Perez-Garcia et al.

Summary: TorchIO is an open-source Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images for deep learning. It encourages good open-science practices, supports experiment reproducibility, and is version-controlled for precise citation. The modular library is compatible with other frameworks for deep learning with medical images.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

Article Engineering, Electrical & Electronic

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging

Rajesh Kumar et al.

Summary: This paper proposes a framework for diagnosing COVID-19 using blockchain-based federated learning. By addressing issues such as data normalization, Capsule Network-based segmentation and classification, and collaborative global model training, the proposed method improves the performance of COVID-19 patient detection.

IEEE SENSORS JOURNAL (2021)

Article Computer Science, Artificial Intelligence

End-to-end privacy preserving deep learning on multi-institutional medical imaging

Georgios Kaissis et al.

Summary: PriMIA is a free, open-source software framework for privacy-preserving medical image analysis, performing well in instance testing and preventing data disclosure.

NATURE MACHINE INTELLIGENCE (2021)

Article Multidisciplinary Sciences

Causality matters in medical imaging

Daniel C. Castro et al.

NATURE COMMUNICATIONS (2020)

Article Computer Science, Artificial Intelligence

Multi-site fMRI analysis using privacy-preserving fe derate d learning and domain adaptation: ABIDE results

Xiaoxiao Li et al.

MEDICAL IMAGE ANALYSIS (2020)

Review Chemistry, Analytical

3D Deep Learning on Medical Images: A Review

Satya P. Singh et al.

SENSORS (2020)

Review Cardiac & Cardiovascular Systems

Image-Based Cardiac Diagnosis With Machine Learning: A Review

Carlos Martin-Isla et al.

FRONTIERS IN CARDIOVASCULAR MEDICINE (2020)

Article Computer Science, Information Systems

Residual Convolutional Neural Network for Cardiac Image Segmentation and Heart Disease Diagnosis

Tao Liu et al.

IEEE ACCESS (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI

Nan Zhang et al.

RADIOLOGY (2019)

Review Cardiac & Cardiovascular Systems

Machine learning in cardiovascular magnetic resonance: basic concepts and applications

Tim Leiner et al.

JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE (2019)

Article Computer Science, Interdisciplinary Applications

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2018)

Article Cardiac & Cardiovascular Systems

Hypertrophic Cardiomyopathy Genetics, Pathogenesis, Clinical Manifestations, Diagnosis, and Therapy

Ali J. Marian et al.

CIRCULATION RESEARCH (2017)

Article Cardiac & Cardiovascular Systems

The need for multicentre cardiovascular clinical trials in Asia

Joey S. W. Kwong et al.

NATURE REVIEWS CARDIOLOGY (2013)

Article Computer Science, Interdisciplinary Applications

N4ITK: Improved N3 Bias Correction

Nicholas J. Tustison et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2010)

Article Computer Science, Interdisciplinary Applications

New variants of a method of MRI scale standardization

LG Nyúl et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2000)