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.
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.
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.
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.
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3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.
Jose Dolz;Christian Desrosiers;Ismail Ben Ayed.
Multiregion level-set partitioning of synthetic aperture radar images
I.B. Ayed;A. Mitiche;Z. Belhadj.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)
Multiregion Image Segmentation by Parametric Kernel Graph Cuts
M B Salah;A Mitiche;I B Ayed.
IEEE Transactions on Image Processing (2011)
HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation
Jose Dolz;Karthik Gopinath;Jing Yuan;Herve Lombaert.
IEEE Transactions on Medical Imaging (2019)
Variational and Level Set Methods in Image Segmentation
Amar Mitiche;Ismail Ben Ayed.
Boundary loss for highly unbalanced segmentation
Hoel Kervadec;Jihene Bouchtiba;Christian Desrosiers;Eric Granger.
International Conference on Medical Imaging with Deep Learning (2019)
Right ventricle segmentation from cardiac MRI: A collation study
Caroline Petitjean;Maria A. Zuluaga;Wenjia Bai;Jean Nicolas Dacher.
Medical Image Analysis (2015)
On Regularized Losses for Weakly-supervised CNN Segmentation
Meng Tang;Federico Perazzi;Abdelaziz Djelouah;Ismail Ben Ayed.
european conference on computer vision (2018)
Constrained-CNN losses for weakly supervised segmentation.
Hoel Kervadec;Jose Dolz;Meng Tang;Eric Granger.
Medical Image Analysis (2019)
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
Jerome Rony;Luiz G. Hafemann;Luiz S. Oliveira;Ismail Ben Ayed.
computer vision and pattern recognition (2019)
Profile was last updated on December 6th, 2021.
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