A review of medical image data augmentation techniques for deep learning applications
Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
Data augmentation for medical imaging: A systematic literature review
Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …
diverse training sets. However, collecting large datasets for medical imaging is still a …
Curriculum learning: A survey
Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …
ones, using curriculum learning can provide performance improvements over the standard …
Data augmentation for brain-tumor segmentation: a review
Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …
Applications of artificial intelligence in cardiovascular imaging
Research into artificial intelligence (AI) has made tremendous progress over the past
decade. In particular, the AI-powered analysis of images and signals has reached human …
decade. In particular, the AI-powered analysis of images and signals has reached human …
3dregnet: A deep neural network for 3d point registration
We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans.
Given a set of 3D point correspondences, we build a deep neural network to address the …
Given a set of 3D point correspondences, we build a deep neural network to address the …
[HTML][HTML] Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
Abstract The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative
to address eminent problems in develo** reliable computer aided detection and diagnosis …
to address eminent problems in develo** reliable computer aided detection and diagnosis …
Artificial intelligence for MR image reconstruction: an overview for clinicians
Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with
recent breakthroughs applying deep‐learning models for data acquisition, classification …
recent breakthroughs applying deep‐learning models for data acquisition, classification …
Deep neural architectures for medical image semantic segmentation
Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …
Review of ECG detection and classification based on deep learning: Coherent taxonomy, motivation, open challenges and recommendations
An electrocardiogram (ECG) is one of the most promising approaches used for the detection
and classification of cardiovascular diseases (CVDs) in recent years. This work reviewed …
and classification of cardiovascular diseases (CVDs) in recent years. This work reviewed …