D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 49 Citations 7,995 268 World Ranking 3060 National Ranking 124

Research.com Recognitions

Awards & Achievements

2010 - ACM Senior Member

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

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 most cited work include:

  • A Survey on Shape Correspondence (473 citations)
  • BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment. (168 citations)
  • Deep features to classify skin lesions (132 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (72.37%)
  • Computer vision (40.53%)
  • Pattern recognition (35.00%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (72.37%)
  • Pattern recognition (35.00%)
  • Deep learning (10.79%)

In recent papers he was focusing on the following fields of study:

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.

Between 2018 and 2021, his most popular works were:

  • Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets (55 citations)
  • Deep semantic segmentation of natural and medical images: a review (47 citations)
  • Combo loss: Handling input and output imbalance in multi-organ segmentation (43 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Machine learning

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A Survey on Shape Correspondence

Oliver van Kaick;Hao Zhang;Ghassan Hamarneh;Daniel Cohen-Or.
Computer Graphics Forum (2011)

616 Citations

BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

Jeremy Kawahara;Colin J. Brown;Steven P. Miller;Brian G. Booth.
NeuroImage (2017)

212 Citations

Deep features to classify skin lesions

Jeremy Kawahara;Aicha BenTaieb;Ghassan Hamarneh.
international symposium on biomedical imaging (2016)

195 Citations

Watershed segmentation using prior shape and appearance knowledge

Ghassan Hamarneh;Xiaoxing Li.
Image and Vision Computing (2009)

185 Citations

$n$ -SIFT: $n$ -Dimensional Scale Invariant Feature Transform

W. Cheung;G. Hamarneh.
IEEE Transactions on Image Processing (2009)

162 Citations

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)

159 Citations

N-SIFT: N-DIMENSIONAL SCALE INVARIANT FEATURE TRANSFORM FOR MATCHING MEDICAL IMAGES

W. Cheung;G. Hamarneh.
international symposium on biomedical imaging (2007)

158 Citations

Deformable organisms for automatic medical image analysis.

Tim McInerney;Tim McInerney;Ghassan Hamarneh;Ghassan Hamarneh;Martha Elizabeth Shenton;Demetri Terzopoulos;Demetri Terzopoulos.
Medical Image Analysis (2002)

143 Citations

Active learning for interactive 3d image segmentation

Andrew Top;Ghassan Hamarneh;Rafeef Abugharbieh.
medical image computing and computer assisted intervention (2011)

142 Citations

VascuSynth: Simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis

Ghassan Hamarneh;Preet Jassi.
Computerized Medical Imaging and Graphics (2010)

136 Citations

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