As machine learning methods gain prominence within clinical decision-mak...
We introduce a novel Region-based contrastive pretraining for Medical Im...
Self-supervised learning in vision-language processing exploits semantic...
Multi-modal data abounds in biomedicine, such as radiology images and
re...
Image registration with deep neural networks has become an active field ...
Quantification of anatomical shape changes still relies on scalar global...
Background: The trend towards large-scale studies including population
i...
In the clinical routine, short axis (SA) cine cardiac MR (CMR) image sta...
We propose a novel attention gate (AG) model for medical image analysis ...
Segmentation of image sequences is an important task in medical image
an...
Alterations in the geometry and function of the heart define well-establ...
Deep learning approaches have shown promising performance for compressed...
Recent advances in deep learning based image segmentation methods have
e...
We propose a fully automatic method to find standardized view planes in ...
In this work, we apply an attention-gated network to real-time automated...
We propose a novel attention gate (AG) model for medical imaging that
au...
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depe...
Deep convolutional neural networks (DCNN) are currently ubiquitous in me...
Cardiovascular magnetic resonance (CMR) imaging is a standard imaging
mo...
Incorporation of prior knowledge about organ shape and location is key t...
3D Magnetic Resonance Imaging (MRI) is often a trade-off between fast bu...
The availability of training data for supervision is a frequently encoun...
In this paper, we propose DeepCut, a method to obtain pixelwise object
s...