The role of generative adversarial networks in brain MRI: a scoping review
The performance of artificial intelligence (AI) for brain MRI can improve if enough data are
made available. Generative adversarial networks (GANs) showed a lot of potential to …
made available. Generative adversarial networks (GANs) showed a lot of potential to …
Transfer learning in magnetic resonance brain imaging: A systematic review
(1) Background: Transfer learning refers to machine learning techniques that focus on
acquiring knowledge from related tasks to improve generalization in the tasks of interest. In …
acquiring knowledge from related tasks to improve generalization in the tasks of interest. In …
Federated and transfer learning for cancer detection based on image analysis
This review highlights the efficacy of combining federated learning (FL) and transfer learning
(TL) for cancer detection via image analysis. By integrating these techniques, research has …
(TL) for cancer detection via image analysis. By integrating these techniques, research has …
An attention-based weakly supervised framework for spitzoid melanocytic lesion diagnosis in whole slide images
R Del Amor, L Launet, A Colomer, A Moscardó… - Artificial intelligence in …, 2021 - Elsevier
Melanoma is an aggressive neoplasm responsible for the majority of deaths from skin
cancer. Specifically, spitzoid melanocytic tumors are one of the most challenging …
cancer. Specifically, spitzoid melanocytic tumors are one of the most challenging …
A systematic literature review on applications of GAN-synthesized images for brain MRI
With the advances in brain imaging, magnetic resonance imaging (MRI) is evolving as a
popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect …
popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect …
[HTML][HTML] Narrative review of generative adversarial networks in medical and molecular imaging
Recent years have witnessed a rapidly expanding use of artificial intelligence and machine
learning in medical imaging. Generative adversarial networks (GANs) are techniques to …
learning in medical imaging. Generative adversarial networks (GANs) are techniques to …
Medical image segmentation with domain adaptation: a survey
Deep learning (DL) has shown remarkable success in various medical imaging data
analysis applications. However, it remains challenging for DL models to achieve good …
analysis applications. However, it remains challenging for DL models to achieve good …
An explainable deep learning-based algorithm with an attention mechanism for predicting the live birth potential of mouse embryos
In assisted reproductive technology (ART), embryos produced by in vitro fertilization (IVF)
are graded according to their live birth potential, and high-grade embryos are preferentially …
are graded according to their live birth potential, and high-grade embryos are preferentially …
Transfer Learning for Neuroimaging via Re-use of Deep Neural Network Features
P Holderrieth, S Smith, H Peng - medRxiv, 2022 - medrxiv.org
A major problem in the application of machine learning to neuroimaging is the technological
variability of MRI scanners and differences in the subject populations across studies …
variability of MRI scanners and differences in the subject populations across studies …
[HTML][HTML] Using Domain Adaptation and Inductive Transfer Learning to Improve Patient Outcome Prediction in the Intensive Care Unit: Retrospective Observational …
Background Accurate patient outcome prediction in the intensive care unit (ICU) can
potentially lead to more effective and efficient patient care. Deep learning models are …
potentially lead to more effective and efficient patient care. Deep learning models are …