What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Ender Konukoglu mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Random forest and Segmentation.
He has included themes like Machine learning and Data mining in his Artificial intelligence study.
His work in Computer vision addresses subjects such as Discriminative model, which are connected to disciplines such as Feature.
His research in Pattern recognition intersects with topics in Lesion, Healthy subjects, Auto encoders and Brain mri.
Ender Konukoglu has researched Random forest in several fields, including Voxel, Anatomy and Regression.
His Segmentation study combines topics from a wide range of disciplines, such as Glioma and Computational model.
His most cited work include:
- The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (1985 citations)
- Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold, Learning and Semi-supervised Learning (571 citations)
- Shape-based hand recognition (275 citations)
What are the main themes of his work throughout his whole career to date?
Ender Konukoglu spends much of his time researching Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Deep learning.
Artificial intelligence and Machine learning are commonly linked in his work.
His Pattern recognition research integrates issues from Probabilistic logic, Magnetic resonance imaging, Undersampling and Benchmark.
His study on Segmentation also encompasses disciplines like
- Cartilage, which have a strong connection to Cohort,
- Supervised learning which is related to area like Semi-supervised learning,
- Falx cerebri which is related to area like Corpus callosum.
The various areas that Ender Konukoglu examines in his Computer vision study include Robot, Random forest, Discriminative model and Capsule endoscopy.
The concepts of his Deep learning study are interwoven with issues in Unsupervised learning, Leverage and Medical imaging.
He most often published in these fields:
- Artificial intelligence (73.86%)
- Pattern recognition (39.20%)
- Segmentation (30.11%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (73.86%)
- Pattern recognition (39.20%)
- Segmentation (30.11%)
In recent papers he was focusing on the following fields of study:
Ender Konukoglu focuses on Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Artificial neural network.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Metric.
His work deals with themes such as Probabilistic logic, Probability density function and Code, which intersect with Pattern recognition.
Many of his research projects under Segmentation are closely connected to Kernel density estimation with Kernel density estimation, tying the diverse disciplines of science together.
His Deep learning research includes elements of Orthodontics, Unsupervised learning, Medical image computing and Spatial contextual awareness.
Ender Konukoglu works mostly in the field of Artificial neural network, limiting it down to topics relating to Training set and, in certain cases, Statistics, Sensitivity to change and Sample size determination, as a part of the same area of interest.
Between 2019 and 2021, his most popular works were:
- Contrastive learning of global and local features for medical image segmentation with limited annotations (23 citations)
- Exploring Cross-Image Pixel Contrast for Semantic Segmentation (12 citations)
- Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain. (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Artificial intelligence, Segmentation, Deep learning, Pattern recognition and Image segmentation.
His work on Feature learning as part of general Artificial intelligence study is frequently connected to Overhead, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His biological study deals with issues like Artificial neural network, which deal with fields such as Training set.
His Deep learning study also includes fields such as
- Leverage that connect with fields like Benchmark, Brain magnetic resonance imaging, Brain mri and Probabilistic logic,
- Unsupervised learning which intersects with area such as Mean squared error, Undersampling and Reconstruction algorithm.
His studies in Pattern recognition integrate themes in fields like Latent variable, Covariance, Image, Statistical model and Image restoration.
His Image segmentation research includes themes of Supervised learning and Standard test image.
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