[HTML][HTML] Deep learning based synthesis of MRI, CT and PET: Review and analysis
Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …
aiming to overcome the challenges associated with acquiring multiple image modalities for …
Deepfakes generation and detection: State-of-the-art, open challenges, countermeasures, and way forward
Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …
modern tools such as Tensorflow or Keras, and open-source trained models, along with …
Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection
Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect …
coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect …
Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives
Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
[HTML][HTML] Application and theory gaps during the rise of artificial intelligence in education
Considering the increasing importance of Artificial Intelligence in Education (AIEd) and the
absence of a comprehensive review on it, this research aims to conduct a comprehensive …
absence of a comprehensive review on it, this research aims to conduct a comprehensive …
[HTML][HTML] An overview of deep learning in medical imaging focusing on MRI
AS Lundervold, A Lundervold - Zeitschrift für Medizinische Physik, 2019 - Elsevier
What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
Generative adversarial network in medical imaging: A review
Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …
community due to their capability of data generation without explicitly modelling the …
Deep learning in medical imaging and radiation therapy
The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
Medical image synthesis for data augmentation and anonymization using generative adversarial networks
Data diversity is critical to success when training deep learning models. Medical imaging
data sets are often imbalanced as pathologic findings are generally rare, which introduces …
data sets are often imbalanced as pathologic findings are generally rare, which introduces …
Current applications and future impact of machine learning in radiology
Recent advances and future perspectives of machine learning techniques offer promising
applications in medical imaging. Machine learning has the potential to improve different …
applications in medical imaging. Machine learning has the potential to improve different …