0000000000349568

AUTHOR

Youssef Skandarani

showing 4 related works from this author

Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI using Deep Convolutional Networks

2020

In this paper, we propose a new deep learning framework for an automatic myocardial infarction evaluation from clinical information and delayed enhancement-MRI (DE-MRI). The proposed framework addresses two tasks. The first task is automatic detection of myocardial contours, the infarcted area, the no-reflow area, and the left ventricular cavity from a short-axis DE-MRI series. It employs two segmentation neural networks. The first network is used to segment the anatomical structures such as the myocardium and left ventricular cavity. The second network is used to segment the pathological areas such as myocardial infarction, myocardial no-reflow, and normal myocardial region. The segmented …

FOS: Computer and information sciencesComputer Vision and Pattern Recognition (cs.CV)Image and Video Processing (eess.IV)Computer Science - Computer Vision and Pattern Recognitioncardiovascular systemFOS: Electrical engineering electronic engineering information engineeringcardiovascular diseasesElectrical Engineering and Systems Science - Image and Video Processing
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Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI Using Deep Convolutional Networks

2021

In this paper, we propose a new deep learning framework for an automatic myocardial infarction evaluation from clinical information and delayed enhancement-MRI (DE-MRI). The proposed framework addresses two tasks. The first task is automatic detection of myocardial contours, the infarcted area, the no-reflow area, and the left ventricular cavity from a short-axis DE-MRI series. It employs two segmentation neural networks. The first network is used to segment the anatomical structures such as the myocardium and left ventricular cavity. The second network is used to segment the pathological areas such as myocardial infarction, myocardial no-reflow, and normal myocardial region. The segmented …

Artificial neural networkComputer sciencebusiness.industryDeep learningPattern recognitionDelayed enhancementmedicine.diseaseSupport vector machineClinical informationcardiovascular systemmedicineLeft ventricular cavitySegmentationcardiovascular diseasesMyocardial infarctionArtificial intelligencebusiness
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Neural Teleportation

2023

In this paper, we explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks. Neural teleportation "teleports" a network to a new position in the weight space and preserves its function. This phenomenon comes directly from the definitions of representation theory applied to neural networks and it turns out to be a very simple operation that has remarkable properties. We shed light on surprising and counter-intuitive consequences neural teleportation has on the loss landscape. In particular, we show that teleportation can be used to explore loss level curves, that it changes the local loss landscape, sharpens global m…

FOS: Computer and information sciencesComputer Science - Machine LearningGeneral MathematicsComputer Science (miscellaneous)Computer Science - Neural and Evolutionary ComputingQuantum PhysicsNeural and Evolutionary Computing (cs.NE)Engineering (miscellaneous)quiver representations; neural networks; teleportationMachine Learning (cs.LG)
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Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?

2021

Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with dif…

FOS: Computer and information sciencesComputer Science - Machine LearningComputer scienceProcess (engineering)GeneralizationIndustrial engineering. Management engineeringComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern Recognitionheartannotated data setT55.4-60.8Machine learningcomputer.software_genre030218 nuclear medicine & medical imagingTheoretical Computer ScienceMachine Learning (cs.LG)Set (abstract data type)03 medical and health sciences0302 clinical medicineFOS: Electrical engineering electronic engineering information engineeringSegmentationNumerical AnalysisArtificial neural networkbusiness.industryDeep learningsegmentationImage and Video Processing (eess.IV)deep learningQA75.5-76.95Electrical Engineering and Systems Science - Image and Video ProcessingComputational MathematicsHausdorff distanceComputational Theory and MathematicsIndex (publishing)Electronic computers. Computer scienceArtificial intelligencebusinesscomputer030217 neurology & neurosurgeryMRI
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