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Found 6 papers, 1 papers with code
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.
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.
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.
Optimization with soft Dice can lead to a volumetric bias
Segmentation is a fundamental task in medical image analysis.
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.
