[HTML][HTML] A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities
Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent
developments in deep neural networks have contributed to significant advances in medical …
developments in deep neural networks have contributed to significant advances in medical …
Artificial intelligence in medicine: where are we now?
S Kulkarni, N Seneviratne, MS Baig, AHA Khan - Academic radiology, 2020 - Elsevier
Artificial intelligence in medicine has made dramatic progress in recent years. However,
much of this progress is seemingly scattered, lacking a cohesive structure for the discerning …
much of this progress is seemingly scattered, lacking a cohesive structure for the discerning …
Deep learning: an update for radiologists
Deep learning is a class of machine learning methods that has been successful in computer
vision. Unlike traditional machine learning methods that require hand-engineered feature …
vision. Unlike traditional machine learning methods that require hand-engineered feature …
Training confounder-free deep learning models for medical applications
The presence of confounding effects (or biases) is one of the most critical challenges in
using deep learning to advance discovery in medical imaging studies. Confounders affect …
using deep learning to advance discovery in medical imaging studies. Confounders affect …
The RSNA international COVID-19 open radiology database (RICORD)
The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency.
Although reverse-transcription polymerase chain reaction testing is the reference standard …
Although reverse-transcription polymerase chain reaction testing is the reference standard …
Post hoc explanations may be ineffective for detecting unknown spurious correlation
We investigate whether three types of post hoc model explanations–feature attribution,
concept activation, and training point ranking–are effective for detecting a model's reliance …
concept activation, and training point ranking–are effective for detecting a model's reliance …
Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
In this work, a unified AI-framework named Hierarchical Deep Learning Neural Network
(HiDeNN) is proposed to solve challenging computational science and engineering …
(HiDeNN) is proposed to solve challenging computational science and engineering …
What the radiologist should know about artificial intelligence–an ESR white paper
… of Radiology (ESR) communications@ myesr. org … - Insights into …, 2019 - Springer
This paper aims to provide a review of the basis for application of AI in radiology, to discuss
the immediate ethical and professional impact in radiology, and to consider possible future …
the immediate ethical and professional impact in radiology, and to consider possible future …
Experimental quantum adversarial learning with programmable superconducting qubits
W Ren, W Li, S Xu, K Wang, W Jiang, F Jin… - Nature Computational …, 2022 - nature.com
Quantum computing promises to enhance machine learning and artificial intelligence.
However, recent theoretical works show that, similar to traditional classifiers based on deep …
However, recent theoretical works show that, similar to traditional classifiers based on deep …
Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations
Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be
practiced in the near future, but several issues need to be resolved before AI can be widely …
practiced in the near future, but several issues need to be resolved before AI can be widely …