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Found 16 papers, 4 papers with code
Convolutional neural networks for medical image segmentation
In this article, we look into some essential aspects of convolutional neural networks (CNNs) with the focus on medical image segmentation.
DeepVoxNet2: Yet another CNN framework
We know that both the CNN mapping function and the sampling scheme are of paramount importance for CNN-based image analysis.
Final infarct prediction in acute ischemic stroke
This article focuses on the control center of each human body: the brain.
Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty
The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation.
The Dice loss in the context of missing or empty labels: Introducing $Φ$ and $ε$
We find and propose heuristic combinations of $\Phi$ and $\epsilon$ that work in a segmentation setting with either missing or empty labels.
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
The test dataset will be used for model validation only and will not be released to the public.
Differentiable Deconvolution for Improved Stroke Perfusion Analysis
We show that our approach is able to generate AIFs without any manual annotation, and hence avoiding manual rater's influences.
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding.
Unsupervised 3D Brain Anomaly Detection
Moreover, we test the potential of the method for detecting other anomalies such as low quality images, preprocessing inaccuracies, artifacts, and even the presence of post-operative signs (such as a craniectomy or a brain shunt).
AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning
Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions.
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
There is an increasing number of studies that propose to use deep learning to provide fast and accurate quantification of COVID-19 using chest CT scans.
Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data
A discriminator model is then trained to predict if two lesion segmentations are based on scans acquired using the same scanner type or not, achieving a 78% accuracy in this task.
Optimization with soft Dice can lead to a volumetric bias
Segmentation is a fundamental task in medical image analysis.
Detection of vertebral fractures in CT using 3D Convolutional Neural Networks
We present a detection method to opportunistically screen spine-containing CT images for the presence of these vertebral fractures.
Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke.
Perfusion parameter estimation using neural networks and data augmentation
Perfusion imaging plays a crucial role in acute stroke diagnosis and treatment decision making.
