This paper introduces vox2vec - a contrastive method for self-supervised...
Deep Learning (DL) models tend to perform poorly when the data comes fro...
When applying a Deep Learning model to medical images, it is crucial to
...
Vertebral body compression fractures are early signs of osteoporosis. Th...
Deep learning methods provide significant assistance in analyzing corona...
We systematically evaluate a Deep Learning (DL) method in a 3D medical i...
Domain shift is one of the most salient challenges in medical computer
v...
Domain Adaptation (DA) methods are widely used in medical image segmenta...
Kotlin is a relatively new programming language from JetBrains: its
deve...
MRI scans appearance significantly depends on scanning protocols and,
co...
Target imbalance affects the performance of recent deep learning methods...
The current COVID-19 pandemic overloads healthcare systems, including
ra...
Vertebral body compression fractures are reliable early signs of
osteopo...
Recent CT Metal Artifacts Reduction (MAR) methods are often based on
ima...
Bug localization is well-known to be a difficult problem in software
eng...
Stereotactic radiosurgery is a minimally-invasive treatment option for a...
Midline shift (MLS) is a well-established factor used for outcome predic...
Quantification of cerebral white matter hyperintensities (WMH) of presum...
Classification-based image retrieval systems are built by training
convo...
Deep learning methods are actively used for brain lesion segmentation. O...
Nowadays, a lot of scientific efforts are concentrated on the diagnosis ...
In the last years, neural networks have proven to be a powerful framewor...
In this work, we study the extent to which structural connectomes and
to...
In the recent years there have been a number of studies that applied dee...
In this paper, we tackle a problem of predicting phenotypes from structu...
We describe GTApprox - a new tool for medium-scale surrogate modeling in...