[HTML][HTML] Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning
Abstract Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting
millions of people's health and lives in jeopardy. Detecting infected patients early on chest …
millions of people's health and lives in jeopardy. Detecting infected patients early on chest …
Communication-efficient vertical federated learning with limited overlapping samples
Federated learning is a popular collaborative learning approach that enables clients to train
a global model without sharing their local data. Vertical federated learning (VFL) deals with …
a global model without sharing their local data. Vertical federated learning (VFL) deals with …
Federated vs local vs central deep learning of tooth segmentation on panoramic radiographs
L Schneider, R Rischke, J Krois, A Krasowski… - Journal of dentistry, 2023 - Elsevier
Abstract Objective Federated Learning (FL) enables collaborative training of artificial
intelligence (AI) models from multiple data sources without directly sharing data. Due to the …
intelligence (AI) models from multiple data sources without directly sharing data. Due to the …
Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology
JS Lim, M Hong, WST Lam, Z Zhang… - Current opinion in …, 2022 - journals.lww.com
AI-applications have vast potential to meet many eyecare needs, consequently reducing
burden on scarce healthcare resources. A pertinent challenge would be to maintain data …
burden on scarce healthcare resources. A pertinent challenge would be to maintain data …
Signds-fl: Local differentially private federated learning with sign-based dimension selection
X Jiang, X Zhou, J Grossklags - ACM Transactions on Intelligent …, 2022 - dl.acm.org
Federated Learning (FL) is a decentralized learning mechanism that has attracted
increasing attention due to its achievements in computational efficiency and privacy …
increasing attention due to its achievements in computational efficiency and privacy …
Applications and challenges of federated learning paradigm in the big data era with special emphasis on COVID-19
A Majeed, X Zhang, SO Hwang - Big Data and Cognitive Computing, 2022 - mdpi.com
Federated learning (FL) is one of the leading paradigms of modern times with higher privacy
guarantees than any other digital solution. Since its inception in 2016, FL has been …
guarantees than any other digital solution. Since its inception in 2016, FL has been …
Computer based diagnosis of some chronic diseases: a medical journey of the last two decades
Disease prediction from diagnostic reports and pathological images using artificial
intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in …
intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in …
Artificial intelligence methodologies for data management
This study analyses the main challenges, trends, technological approaches, and artificial
intelligence methods developed by new researchers and professionals in the field of …
intelligence methods developed by new researchers and professionals in the field of …
A survey of federated learning from data perspective in the healthcare domain: Challenges, methods, and future directions
Recent advances in deep learning (DL) have shown that data-driven insights can be used in
smart healthcare applications to improve the quality of life for patients. DL needs more data …
smart healthcare applications to improve the quality of life for patients. DL needs more data …
Guest editorial annotation-efficient deep learning: the holy grail of medical imaging
Annotation-efficient deep learning refers to methods and practices that yield high-
performance deep learning models without the use of massive carefully labeled training …
performance deep learning models without the use of massive carefully labeled training …