[Retracted] U‐Net‐Based Medical Image Segmentation
Deep learning has been extensively applied to segmentation in medical imaging. U‐Net
proposed in 2015 shows the advantages of accurate segmentation of small targets and its …
proposed in 2015 shows the advantages of accurate segmentation of small targets and its …
Monai: An open-source framework for deep learning in healthcare
Artificial Intelligence (AI) is having a tremendous impact across most areas of science.
Applications of AI in healthcare have the potential to improve our ability to detect, diagnose …
Applications of AI in healthcare have the potential to improve our ability to detect, diagnose …
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Biomedical imaging is a driver of scientific discovery and a core component of medical care
and is being stimulated by the field of deep learning. While semantic segmentation …
and is being stimulated by the field of deep learning. While semantic segmentation …
[HTML][HTML] TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Background and ObjectiveProcessing of medical images such as MRI or CT presents
different challenges compared to RGB images typically used in computer vision. These …
different challenges compared to RGB images typically used in computer vision. These …
Flamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings
J Ogier du Terrail, SS Ayed, E Cyffers… - Advances in …, 2022 - proceedings.neurips.cc
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data
to collaboratively train machine learning models, without centralizing data. The cross-silo FL …
to collaboratively train machine learning models, without centralizing data. The cross-silo FL …
Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey
Radiologists encounter significant challenges when segmenting and determining brain
tumors in patients because this information assists in treatment planning. The utilization of …
tumors in patients because this information assists in treatment planning. The utilization of …
VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images
Vertebral labelling and segmentation are two fundamental tasks in an automated spine
processing pipeline. Reliable and accurate processing of spine images is expected to …
processing pipeline. Reliable and accurate processing of spine images is expected to …
MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning
Background The increased availability and usage of modern medical imaging induced a
strong need for automatic medical image segmentation. Still, current image segmentation …
strong need for automatic medical image segmentation. Still, current image segmentation …
[HTML][HTML] Robust chest CT image segmentation of COVID-19 lung infection based on limited data
Background The coronavirus disease 2019 (COVID-19) affects billions of lives around the
world and has a significant impact on public healthcare. For quantitative assessment and …
world and has a significant impact on public healthcare. For quantitative assessment and …
Studying robustness of semantic segmentation under domain shift in cardiac MRI
Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in many heart
related diseases. Recently, deep neural networks have demonstrated successful automatic …
related diseases. Recently, deep neural networks have demonstrated successful automatic …