Collaborative privacy-preserving approaches for distributed deep learning using multi-institutional data
Deep learning (DL) algorithms have shown remarkable potential in automating various tasks
in medical imaging and radiologic reporting. However, models trained on low quantities of …
in medical imaging and radiologic reporting. However, models trained on low quantities of …
Deep learning-based artificial intelligence applications in prostate MRI: brief summary
Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI
has an established role in diagnosis of PCa through guiding biopsies. Due to multistep …
has an established role in diagnosis of PCa through guiding biopsies. Due to multistep …
Proportionally fair hospital collaborations in federated learning of histopathology images
Medical centers and healthcare providers have concerns and hence restrictions around
sharing data with external collaborators. Federated learning, as a privacy-preserving …
sharing data with external collaborators. Federated learning, as a privacy-preserving …
[HTML][HTML] Self-supervised spatial–temporal transformer fusion based federated framework for 4D cardiovascular image segmentation
Availability of high-quality large annotated data is indeed a significant challenge in
healthcare. In addition, privacy concerns and data-sharing restrictions often hinder access to …
healthcare. In addition, privacy concerns and data-sharing restrictions often hinder access to …
FedPerl: Semi-supervised peer learning for skin lesion classification
Skin cancer is one of the most deadly cancers worldwide. Yet, it can be reduced by early
detection. Recent deep-learning methods have shown a dermatologist-level performance in …
detection. Recent deep-learning methods have shown a dermatologist-level performance in …
Feddis: Disentangled federated learning for unsupervised brain pathology segmentation
In recent years, data-driven machine learning (ML) methods have revolutionized the
computer vision community by providing novel efficient solutions to many unsolved …
computer vision community by providing novel efficient solutions to many unsolved …
Artificial intelligence for dermatopathology: Current trends and the road ahead
SB Chen, RA Novoa - Seminars in Diagnostic Pathology, 2022 - Elsevier
Artificial intelligence (AI), including deep learning methods that leverage neural network-
based algorithms, hold significant promise for dermatopathology and other areas of …
based algorithms, hold significant promise for dermatopathology and other areas of …
Empowering federated learning for massive models with nvidia flare
In the ever-evolving landscape of artificial intelligence (AI) and large language models
(LLMs), handling and leveraging data effectively has become a critical challenge. Most state …
(LLMs), handling and leveraging data effectively has become a critical challenge. Most state …
MERGE: A model for multi-input biomedical federated learning
Driven by the deep learning (DL) revolution, artificial intelligence (AI) has become a
fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic …
fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic …
In the pursuit of privacy: the promises and predicaments of federated learning in healthcare
MY Topaloglu, EM Morrell, S Rajendran… - Frontiers in Artificial …, 2021 - frontiersin.org
Artificial Intelligence and its subdomain, Machine Learning (ML), have shown the potential to
make an unprecedented impact in healthcare. Federated Learning (FL) has been introduced …
make an unprecedented impact in healthcare. Federated Learning (FL) has been introduced …