Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines
Advancements in deep learning techniques carry the potential to make significant
contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis …
contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis …
[HTML][HTML] A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues
S Shamshirband, M Fathi, A Dehzangi… - Journal of Biomedical …, 2021 - Elsevier
In the last few years, the application of Machine Learning approaches like Deep Neural
Network (DNN) models have become more attractive in the healthcare system given the …
Network (DNN) models have become more attractive in the healthcare system given the …
Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …
problems for people with a detrimental effect on the functioning of the nervous system. In …
Networking architecture and key supporting technologies for human digital twin in personalized healthcare: a comprehensive survey
Digital twin (DT), referring to a promising technique to digitally and accurately represent
actual physical entities, has attracted explosive interests from both academia and industry …
actual physical entities, has attracted explosive interests from both academia and industry …
Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review
The rapid development of diagnostic technologies in healthcare is leading to higher
requirements for physicians to handle and integrate the heterogeneous, yet complementary …
requirements for physicians to handle and integrate the heterogeneous, yet complementary …
Multiple sclerosis diagnosis using machine learning and deep learning: challenges and opportunities
Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which
can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated …
can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated …
A review of the application of multi-modal deep learning in medicine: bibliometrics and future directions
In recent years, deep learning has been applied in the field of clinical medicine to process
large-scale medical images, for large-scale data screening, and in the diagnosis and …
large-scale medical images, for large-scale data screening, and in the diagnosis and …
Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine
I Zafar, S Anwar, W Yousaf, FU Nisa, T Kausar… - … Signal Processing and …, 2023 - Elsevier
The advancements in genomics and biomedical technologies have generated vast amounts
of biological and physiological data, which present opportunities for understanding human …
of biological and physiological data, which present opportunities for understanding human …
Artificial intelligence and deep learning in neuroradiology: exploring the new frontier
H Kaka, E Zhang, N Khan - Canadian Association of …, 2021 - journals.sagepub.com
There have been many recently published studies exploring machine learning (ML) and
deep learning applications within neuroradiology. The improvement in performance of these …
deep learning applications within neuroradiology. The improvement in performance of these …
End-to-end learning of fused image and non-image features for improved breast cancer classification from mri
Breast cancer diagnosis is inherently multimodal. To assess a patient's cancer status,
physicians integrate imaging findings with a variety of clinical risk factor data. Despite this …
physicians integrate imaging findings with a variety of clinical risk factor data. Despite this …