2010 - ACM Senior Member
His primary areas of study are Artificial intelligence, Segmentation, Computer vision, Pattern recognition and Image segmentation. His work is connected to Convolutional neural network, Deep learning, Image, Medial axis and Image processing, as a part of Artificial intelligence. His Segmentation study integrates concerns from other disciplines, such as Computer-aided diagnosis and Curvature.
Ghassan Hamarneh combines subjects such as Visualization and Artificial life with his study of Computer vision. His biological study spans a wide range of topics, including Content, Base and Face. His research investigates the connection with Image segmentation and areas like Medical imaging which intersect with concerns in Robustness.
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image segmentation. Many of his studies involve connections with topics such as Machine learning and Artificial intelligence. His study brings together the fields of Artificial life and Computer vision.
His Pattern recognition research is multidisciplinary, incorporating elements of Artificial neural network, Probabilistic logic and Skin lesion. His Segmentation research is multidisciplinary, relying on both Medical imaging, Pixel, Jaccard index, Ground truth and Robustness. His Active shape model research is multidisciplinary, incorporating perspectives in Shape analysis and Point distribution model.
Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Convolutional neural network are his primary areas of study. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Magnetic resonance imaging and Computer vision. As part of the same scientific family, Ghassan Hamarneh usually focuses on Computer vision, concentrating on Sensory cue and intersecting with Eyebrow and Vowel.
He has included themes like Regularization, Fluorescence, Microscopy, Image and Confocal microscopy in his Pattern recognition study. Ghassan Hamarneh has researched Deep learning in several fields, including Markov process, Medical imaging, Encoding and Skin lesion. His research in Segmentation is mostly focused on Image segmentation.
His main research concerns Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Image segmentation. Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Magnetic resonance imaging. The study incorporates disciplines such as Regularization, Leverage, Projection and Invagination in addition to Pattern recognition.
His work carried out in the field of Deep learning brings together such families of science as Image synthesis, Skin cancer, Medical imaging, Glioma and Skin lesion. Ghassan Hamarneh interconnects False positive paradox, Cross entropy and Voxel in the investigation of issues within Image segmentation. His Convolutional neural network research includes themes of Basis, Metric and Robustness.
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A Survey on Shape Correspondence
Oliver van Kaick;Hao Zhang;Ghassan Hamarneh;Daniel Cohen-Or.
Computer Graphics Forum (2011)
BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.
Jeremy Kawahara;Colin J. Brown;Steven P. Miller;Brian G. Booth.
Deep features to classify skin lesions
Jeremy Kawahara;Aicha BenTaieb;Ghassan Hamarneh.
international symposium on biomedical imaging (2016)
Deep semantic segmentation of natural and medical images: a review
Saeid Asgari Taghanaki;Kumar Abhishek;Joseph Paul Cohen;Julien Cohen-Adad.
Artificial Intelligence Review (2021)
Watershed segmentation using prior shape and appearance knowledge
Ghassan Hamarneh;Xiaoxing Li.
Image and Vision Computing (2009)
$n$ -SIFT: $n$ -Dimensional Scale Invariant Feature Transform
W. Cheung;G. Hamarneh.
IEEE Transactions on Image Processing (2009)
N-SIFT: N-DIMENSIONAL SCALE INVARIANT FEATURE TRANSFORM FOR MATCHING MEDICAL IMAGES
W. Cheung;G. Hamarneh.
international symposium on biomedical imaging (2007)
Segmentation of Intra-Retinal Layers From Optical Coherence Tomography Images Using an Active Contour Approach
A Yazdanpanah;G Hamarneh;B R Smith;M V Sarunic.
IEEE Transactions on Medical Imaging (2011)
Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets
Jeremy Kawahara;Sara Daneshvar;Giuseppe Argenziano;Ghassan Hamarneh.
IEEE Journal of Biomedical and Health Informatics (2019)
Active learning for interactive 3d image segmentation
Andrew Top;Ghassan Hamarneh;Rafeef Abugharbieh.
medical image computing and computer assisted intervention (2011)
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