Lessons learned in transitioning to AI in the medical imaging of COVID-19
The coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc across the world.
It also created a need for the urgent development of efficacious predictive diagnostics …
It also created a need for the urgent development of efficacious predictive diagnostics …
Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data
Artificial intelligence (AI) is currently regaining enormous interest due to the success of
machine learning (ML), and in particular deep learning (DL). Image analysis, and thus …
machine learning (ML), and in particular deep learning (DL). Image analysis, and thus …
Explainable artificial intelligence to increase transparency for revolutionizing healthcare ecosystem and the road ahead
The integration of deep learning (DL) into co-clinical applications has generated substantial
interest among researchers aiming to enhance clinical decision support systems for various …
interest among researchers aiming to enhance clinical decision support systems for various …
The present and future of deep learning in radiology
Abstract The advent of Deep Learning (DL) is poised to dramatically change the delivery of
healthcare in the near future. Not only has DL profoundly affected the healthcare industry it …
healthcare in the near future. Not only has DL profoundly affected the healthcare industry it …
[HTML][HTML] Personalizing medicine through hybrid imaging and medical big data analysis
Medical imaging has evolved from a pure visualization tool to representing a primary source
of analytic approaches toward in vivo disease characterization. Hybrid imaging is an integral …
of analytic approaches toward in vivo disease characterization. Hybrid imaging is an integral …
Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model …
Purpose To recognize and address various sources of bias essential for algorithmic fairness
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …
In the era of deep learning, why reconstruct an image at all?
In the pursuit of precision medicine, the value of integrating a wide variety of sources of data
and using quantitative approaches has been emphasized. Imaging has had a rapidly …
and using quantitative approaches has been emphasized. Imaging has had a rapidly …
[HTML][HTML] Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios
Deep neural networks (DNNs) have already impacted the field of medicine in data analysis,
classification, and image processing. Unfortunately, their performance is drastically reduced …
classification, and image processing. Unfortunately, their performance is drastically reduced …
[HTML][HTML] A promising and challenging approach: radiologists' perspective on deep learning and artificial intelligence for fighting COVID-19
T Wang, Z Chen, Q Shang, C Ma, X Chen, E Xiao - Diagnostics, 2021 - mdpi.com
Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging
modalities used against the increased worldwide spread of the 2019 coronavirus disease …
modalities used against the increased worldwide spread of the 2019 coronavirus disease …
Advances in deep learning-based medical image analysis
Importance. With the booming growth of artificial intelligence (AI), especially the recent
advancements of deep learning, utilizing advanced deep learning-based methods for …
advancements of deep learning, utilizing advanced deep learning-based methods for …