What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Ismail Ben Ayed spends much of his time researching Segmentation, Artificial intelligence, Algorithm, Image segmentation and Pattern recognition.
Many of his research projects under Segmentation are closely connected to Graph with Graph, tying the diverse disciplines of science together.
His Artificial intelligence research focuses on Computer vision and how it connects with Ventricle.
His Algorithm research incorporates elements of Mathematical optimization and Cut.
His Image segmentation study combines topics from a wide range of disciplines, such as Pixel and Real image.
His study focuses on the intersection of Pattern recognition and fields such as Contextual image classification with connections in the field of Artificial neural network, Modality, Feature learning, Cardiac cycle and Linear discriminant analysis.
His most cited work include:
- 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study. (227 citations)
- Multiregion level-set partitioning of synthetic aperture radar images (183 citations)
- Multiregion Image Segmentation by Parametric Kernel Graph Cuts (171 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Segmentation, Pattern recognition, Algorithm and Image segmentation.
His studies in Artificial intelligence integrate themes in fields like Machine learning, Magnetic resonance imaging and Computer vision.
In his research on the topic of Segmentation, Matching is strongly related with Similarity.
His work investigates the relationship between Pattern recognition and topics such as Differential entropy that intersect with problems in Kalman filter.
Ismail Ben Ayed has researched Algorithm in several fields, including Boundary, Mathematical optimization, Cluster analysis and Pairwise comparison.
His Image segmentation study integrates concerns from other disciplines, such as Synthetic aperture radar, Real image and Image processing.
He most often published in these fields:
- Artificial intelligence (68.04%)
- Segmentation (54.12%)
- Pattern recognition (37.11%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (68.04%)
- Segmentation (54.12%)
- Pattern recognition (37.11%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Segmentation, Pattern recognition, Deep learning and Algorithm.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Computer vision.
Ismail Ben Ayed is studying Image segmentation, which is a component of Segmentation.
His work on Convolutional neural network as part of general Pattern recognition research is frequently linked to Fraction, bridging the gap between disciplines.
His work carried out in the field of Deep learning brings together such families of science as Supervised learning, Partial volume, Brain mri and Cutting-plane method.
His Algorithm study incorporates themes from Boundary, Pairwise comparison and Softmax function.
Between 2019 and 2021, his most popular works were:
- Boundary loss for highly unbalanced segmentation. (31 citations)
- Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation (30 citations)
- Deep Clustering: On the Link Between Discriminative Models and K-Means. (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Algorithm, Segmentation, Deep learning and Machine learning.
Ismail Ben Ayed studied Artificial intelligence and Pattern recognition that intersect with Image quality.
His Algorithm research incorporates themes from Weighting, Information theory, Pairwise comparison and Entropy.
Particularly relevant to Image segmentation is his body of work in Segmentation.
His studies deal with areas such as Supervised learning, Cross entropy, Boundary and Computation as well as Deep learning.
His Machine learning study integrates concerns from other disciplines, such as Bilevel optimization, Classifier, Learning data, Contextual image classification and Domain knowledge.
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.
