Results 21 to 30 of about 12,340,531 (359)
Machine learning for medical imaging: methodological failures and recommendations for the future
npj Digital Medicine, 2022 Research in computer analysis of medical images bears many promises to improve patients’ health. However, a number of systematic challenges are slowing down the progress of the field, from limitations of the data, such as biases, to research incentives ...
G. Varoquaux, V. Cheplygina
semanticscholar +1 more source
iScience, 2022 Summary: Non-human primates (NHPs) are particularly relevant as preclinical models for SARS-CoV-2 infection and nuclear imaging may represent a valuable tool for monitoring infection in this species.
Thibaut Naninck +19 more
doaj +1 more source
Addressing fairness in artificial intelligence for medical imaging
Nature Communications, 2022 A plethora of work has shown that AI systems can systematically and unfairly be biased against certain populations in multiple scenarios. The field of medical imaging, where AI systems are beginning to be increasingly adopted, is no exception.
M. A. Ricci Lara +2 more
semanticscholar +1 more source
Neural Plasticity, 2023 This study was aimed at investigating the functional connectivity (FC) changes between the insular subregions and whole brain in patients with obstructive sleep apnea (OSA) after 6 months of continuous positive airway pressure (CPAP) treatment and at ...
Ting Long +9 more
doaj +1 more source
Advances of gold nanoclusters for bioimaging
iScience, 2022 Summary: Gold nanoclusters (AuNCs) have become a promising material for bioimaging detection because of their tunable photoluminescence, large Stokes shift, low photobleaching, and good biocompatibility.
Cheng Zhang +6 more
doaj +1 more source
Self-supervised learning methods and applications in medical imaging analysis: a survey [PDF]
PeerJ Computer Science, 2021 The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement.
Saeed Shurrab, R. Duwairi
semanticscholar +1 more source
End-to-end privacy preserving deep learning on multi-institutional medical imaging
Nature Machine Intelligence, 2021 Using large, multi-national datasets for high-performance medical imaging AI systems requires innovation in privacy-preserving machine learning so models can train on sensitive data without requiring data transfer.
Georgios Kaissis +13 more
semanticscholar +1 more source
, 2022 This chapter presents deep learning methodologies for medical imaging tasks. The chapter starts with echocardiography for early detection of myocardial infarction (MI) or commonly known as heart attack. Early and fundamental signs of MI can be visible as the abnormality in one or several segments of the left ventricle (LV) wall, where a segment may ...
Degerli Aysen +4 more
openaire +3 more sources
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
npj Digital Medicine, 2021 Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted
R. Aggarwal +7 more
semanticscholar +1 more source
Speckle filtering techniques for different quality level of healthy kidney ultrasound images [PDF]
, 2019 The increasing reliance of modern medicine on diagnostic techniques such as computerized tomography, histopathology, magnetic resonance imaging, radiology and ultrasound imaging shows the importance of medical images [1].
Nazari, Ain +2 more
core +1 more source