Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …
processing algorithms, and deep learning based models have been remarkably successful …
Machine learning in additive manufacturing: State-of-the-art and perspectives
Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology.
However, its broad adoption in industry is still hindered by high entry barriers of design for …
However, its broad adoption in industry is still hindered by high entry barriers of design for …
[HTML][HTML] Deep learning for topology optimization of 2D metamaterials
HT Kollmann, DW Abueidda, S Koric, E Guleryuz… - Materials & Design, 2020 - Elsevier
Data-driven models are rising as an auspicious method for the geometrical design of
materials and structural systems. Nevertheless, existing data-driven models customarily …
materials and structural systems. Nevertheless, existing data-driven models customarily …
[HTML][HTML] A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images
Abstract Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and
mankind all over the world is vulnerable to this virus. Alternative tools are needed that can …
mankind all over the world is vulnerable to this virus. Alternative tools are needed that can …
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …
medical imaging. While medical imaging datasets have been growing in size, a challenge …
Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia
Alzheimer's disease (AD) is one of the most common form of dementia which mostly affects
elderly people. AD identification in early stages is a difficult task in medical practice and …
elderly people. AD identification in early stages is a difficult task in medical practice and …
Medical students' attitude towards artificial intelligence: a multicentre survey
Objectives To assess undergraduate medical students' attitudes towards artificial
intelligence (AI) in radiology and medicine. Materials and methods A web-based …
intelligence (AI) in radiology and medicine. Materials and methods A web-based …
[HTML][HTML] Machine learning for dementia prediction: a systematic review and future research directions
Abstract Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully
provided automated solutions to numerous real-world problems. Healthcare is one of the …
provided automated solutions to numerous real-world problems. Healthcare is one of the …
Medical image classification using synergic deep learning
The classification of medical images is an essential task in computer-aided diagnosis,
medical image retrieval and mining. Although deep learning has shown proven advantages …
medical image retrieval and mining. Although deep learning has shown proven advantages …
Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography–based fractional flow reserve: result from the MACHINE …
Background: Coronary computed tomographic angiography (CTA) is a reliable modality to
detect coronary artery disease. However, CTA generally overestimates stenosis severity …
detect coronary artery disease. However, CTA generally overestimates stenosis severity …