0000000000598419

AUTHOR

Tegolo

showing 2 related works from this author

A visual framework to create photorealistic retinal vessels for diagnosis purposes

2020

The methods developed in recent years for synthesising an ocular fundus can be been divided into two main categories. The first category of methods involves the development of an anatomical model of the eye, where artificial images are generated using appropriate parameters for modelling the vascular networks and fundus. The second type of method has been made possible by the development of deep learning techniques and improvements in the performance of hardware (especially graphics cards equipped with a large number of cores). The methodology proposed here to produce high-resolution synthetic fundus images is intended to be an alternative to the increasingly widespread use of generative ad…

PLUS DISEASEData augmentationFundus OculiComputer scienceCOMPUTER-AIDED DIAGNOSISIMAGESSEGMENTATIONComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONHealth InformaticsSynthetic retinal imageFundus (eye)Fundus image analysisStatistical featuresTORTUOSITY03 medical and health sciences0302 clinical medicineImage Processing Computer-AssistedComputer vision030212 general & internal medicineGraphics030304 developmental biologyGraphical user interfaceSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni0303 health sciencesSettore INF/01 - Informaticabusiness.industryDeep learningRetinal VesselsReal imageComputer Science ApplicationsPredictive evaluation diseasesFILTERA priori and a posterioriArtificial intelligencebusinessSYSTEMJournal of Biomedical Informatics
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Graph-theoretical derivation of brain structural connectivity

2020

Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense efforts in the field, no mathematical description has been obtained so far. Here, we present a mathematical framework based on a graph-theoretical approach that, starting from experimental data obtained from a few small subsets of neurons, can quantitatively explain and predict the corresponding full network properties. This model also changes the paradigm with which large-scale model networks can be built, from using probabilisti…

0209 industrial biotechnologyTheoretical computer scienceComputer scienceNeuronal network02 engineering and technologyMECHANISMSCENTRALITY020901 industrial engineering & automationSettore MAT/05 - Analisi MatematicaNeuronal networksConnectome0202 electrical engineering electronic engineering information engineeringINDEXComputer Science::DatabasesRandom graphsSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaQuantitative Biology::Neurons and CognitionApplied MathematicsProbabilistic logicExperimental data020206 networking & telecommunicationsComputational MathematicsSYNCHRONIZATIONSIMULATIONGraph (abstract data type)Applied Mathematics and Computation
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