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.
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