[HTML][HTML] PDAtt-Unet: Pyramid dual-decoder attention Unet for Covid-19 infection segmentation from CT-scans
Since the emergence of the Covid-19 pandemic in late 2019, medical imaging has been
widely used to analyze this disease. Indeed, CT-scans of the lungs can help diagnose …
widely used to analyze this disease. Indeed, CT-scans of the lungs can help diagnose …
Auto-fedrl: Federated hyperparameter optimization for multi-institutional medical image segmentation
Federated learning (FL) is a distributed machine learning technique that enables
collaborative model training while avoiding explicit data sharing. The inherent privacy …
collaborative model training while avoiding explicit data sharing. The inherent privacy …
Pseudo-label guided image synthesis for semi-supervised covid-19 pneumonia infection segmentation
Coronavirus disease 2019 (COVID-19) has become a severe global pandemic. Accurate
pneumonia infection segmentation is important for assisting doctors in diagnosing COVID …
pneumonia infection segmentation is important for assisting doctors in diagnosing COVID …
Distance-based detection of out-of-distribution silent failures for covid-19 lung lesion segmentation
Automatic segmentation of ground glass opacities and consolidations in chest computer
tomography (CT) scans can potentially ease the burden of radiologists during times of high …
tomography (CT) scans can potentially ease the burden of radiologists during times of high …
Customized efficient neural network for COVID-19 infected region identification in CT images
Background: In the field of biomedical imaging, radiomics is a promising approach that aims
to provide quantitative features from images. It is highly dependent on accurate identification …
to provide quantitative features from images. It is highly dependent on accurate identification …
AdaD-FNN for chest CT-based COVID-19 diagnosis
Coronavirus disease 2019 (COVID-19) generated a global public health emergency since
December 2019, causing huge economic losses. To help radiologists strengthen their …
December 2019, causing huge economic losses. To help radiologists strengthen their …
CaraNet: context axial reverse attention network for segmentation of small medical objects
Purpose Segmenting medical images accurately and reliably is important for disease
diagnosis and treatment. It is a challenging task because of the wide variety of objects' sizes …
diagnosis and treatment. It is a challenging task because of the wide variety of objects' sizes …
Domain and content adaptive convolution based multi-source domain generalization for medical image segmentation
The domain gap caused mainly by variable medical image quality renders a major obstacle
on the path between training a segmentation model in the lab and applying the trained …
on the path between training a segmentation model in the lab and applying the trained …
CARes‐UNet: Content‐aware residual UNet for lesion segmentation of COVID‐19 from chest CT images
Abstract Purpose Coronavirus disease 2019 (COVID‐19) has caused a serious global
health crisis. It has been proven that the deep learning method has great potential to assist …
health crisis. It has been proven that the deep learning method has great potential to assist …
COVID detection and severity prediction with 3D-ConvNeXt and custom pretrainings
D Kienzle, J Lorenz, R Schön, K Ludwig… - European Conference on …, 2022 - Springer
Since COVID strongly affects the respiratory system, lung CT-scans can be used for the
analysis of a patients health. We introduce a neural network for the prediction of the severity …
analysis of a patients health. We introduce a neural network for the prediction of the severity …