Fabrice Meriaudeau mainly investigates Artificial intelligence, Computer vision, Optics, Diabetic retinopathy and Segmentation. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Microaneurysm and Pattern recognition. The Computer vision study combines topics in areas such as Retina, Optical coherence tomography and Fundus.
His work on Diabetic macular edema as part of general Diabetic retinopathy research is frequently linked to Population, bridging the gap between disciplines. The concepts of his Segmentation study are interwoven with issues in Radiology, Prostate, Speckle pattern and Atlas. His Support vector machine research focuses on subjects like Principal component analysis, which are linked to Bag-of-words model, Histogram of oriented gradients, Feature detection, Feature and Correlation.
Fabrice Meriaudeau focuses on Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Optics. His Artificial intelligence study deals with Retinal intersecting with Retina. His study ties his expertise on Diabetic retinopathy together with the subject of Computer vision.
His Diabetic retinopathy research is multidisciplinary, incorporating elements of Computer-aided diagnosis and Fundus. In his research, Prostate cancer and Prostate biopsy is intimately related to Magnetic resonance imaging, which falls under the overarching field of Segmentation. His Pattern recognition study combines topics in areas such as Local binary patterns and Optical coherence tomography.
His main research concerns Artificial intelligence, Deep learning, Computer vision, Segmentation and Pattern recognition. Fabrice Meriaudeau interconnects Retinal and Optical coherence tomography in the investigation of issues within Artificial intelligence. His Deep learning study integrates concerns from other disciplines, such as Object detection and Medical imaging.
He has researched Computer vision in several fields, including Polarimetry and Refraction. His Segmentation study which covers Prostate that intersects with Radiology. His work carried out in the field of Pattern recognition brings together such families of science as Artificial neural network, Autoencoder and Speech recognition.
Fabrice Meriaudeau mainly focuses on Artificial intelligence, Computer vision, Deep learning, Pattern recognition and Optical coherence tomography. His research on Artificial intelligence frequently links to adjacent areas such as Optic disc. His work deals with themes such as Photoplethysmogram, Signal and Underwater vehicle, which intersect with Computer vision.
His Deep learning study also includes fields such as
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Exudate-based diabetic macular edema detection in fundus images using publicly available datasets.
Luca Giancardo;Fabrice Meriaudeau;Thomas Paul Karnowski;Yaquin Li.
Medical Image Analysis (2012)
Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research
Prasanna Porwal;Samiksha Pachade;Ravi Kamble;Manesh Kokare.
international conference on data technologies and applications (2018)
Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI
Guillaume Lemaître;Robert Martí;Jordi Freixenet;Joan C. Vilanova.
Computers in Biology and Medicine (2015)
A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images
Soumya Ghose;Arnau Oliver;Robert Martí;Xavier Lladó.
Computer Methods and Programs in Biomedicine (2012)
Validating retinal fundus image analysis algorithms: issues and a proposal.
Emanuele Trucco;Alfredo Ruggeri;Thomas Karnowski;Luca Giancardo.
Investigative Ophthalmology & Visual Science (2013)
Active lighting applied to three-dimensional reconstruction of specular metallic surfaces by polarization imaging.
Olivier Morel;Christophe Stolz;Fabrice Meriaudeau;Patrick Gorria.
Applied Optics (2006)
Heart rate estimation using facial video: A review
Mohamed Abul Hassan;Mohamed Abul Hassan;Aamir Saeed Malik;David Fofi;Naufal Saad.
Biomedical Signal Processing and Control (2017)
Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection
Guillaume Lemaître;Mojdeh Rastgoo;Joan Massich;Carol Y. Cheung.
Journal of Ophthalmology (2016)
Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning
Kedir M. Adal;Désiré Sidibé;Sharib Ali;Edward Chaum.
Computer Methods and Programs in Biomedicine (2014)
Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models
Kai-kai Shen;Jurgen Fripp;Fabrice Mériaudeau;Gaël Chételat.
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