H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 56 Citations 19,759 201 World Ranking 2042 National Ranking 114

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Computer vision

His main research concerns Artificial intelligence, Segmentation, Pattern recognition, Computer vision and Machine learning. His Artificial intelligence study frequently draws connections to adjacent fields such as Domain. His studies deal with areas such as Artificial neural network, Ground truth and Magnetic resonance imaging as well as Segmentation.

His Pattern recognition study combines topics in areas such as Metric, Cortical surface, Projection, Smoothness and Image. His research in Computer vision intersects with topics in Discriminative model and Generative model. In Image segmentation, Ben Glocker works on issues like Medical imaging, which are connected to Benchmark and Brain tumor.

His most cited work include:

  • The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (1985 citations)
  • Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation (1604 citations)
  • Attention U-Net: Learning Where to Look for the Pancreas (581 citations)

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

The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Machine learning. His Artificial intelligence research focuses on Image segmentation, Deep learning, Convolutional neural network, Medical imaging and Artificial neural network. Ben Glocker has researched Pattern recognition in several fields, including Domain, Leverage, Embedding, Image and Neuroimaging.

His Segmentation research includes themes of Ground truth, Magnetic resonance imaging and Overfitting. His Computer vision study combines topics from a wide range of disciplines, such as Random forest and Robustness. His Image registration research incorporates elements of Markov random field and Mathematical optimization.

He most often published in these fields:

  • Artificial intelligence (75.89%)
  • Pattern recognition (33.69%)
  • Segmentation (29.79%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (75.89%)
  • Segmentation (29.79%)
  • Pattern recognition (33.69%)

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

Ben Glocker focuses on Artificial intelligence, Segmentation, Pattern recognition, Deep learning and Image segmentation. Ben Glocker has included themes like Machine learning, Cranioplasty and Computer vision in his Artificial intelligence study. In general Computer vision, his work in Three-dimensional space is often linked to Task linking many areas of study.

When carried out as part of a general Segmentation research project, his work on Sørensen–Dice coefficient is frequently linked to work in Affine transformation, therefore connecting diverse disciplines of study. He combines subjects such as Transformer and Identification with his study of Pattern recognition. His biological study spans a wide range of topics, including Normalization, Normalization, Anomaly detection and Ground truth.

Between 2019 and 2021, his most popular works were:

  • Unpaired Multi-Modal Segmentation via Knowledge Distillation (33 citations)
  • Causality matters in medical imaging. (21 citations)
  • Post-DAE: Anatomically Plausible Segmentation via Post-Processing With Denoising Autoencoders (13 citations)

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

  • Artificial intelligence
  • Statistics
  • Computer vision

Ben Glocker mainly investigates Artificial intelligence, Traumatic brain injury, Segmentation, Radiology and Pattern recognition. Ben Glocker performs integrative study on Artificial intelligence and Causation in his works. His work carried out in the field of Traumatic brain injury brings together such families of science as Glasgow Coma Scale, Emergency department and Tracheal intubation.

The study incorporates disciplines such as Pixel, Random forest and Nonlinear dimensionality reduction in addition to Segmentation. In general Radiology study, his work on Image-guided radiation therapy, Radiation therapy and Radiation oncologist often relates to the realm of Validation study, thereby connecting several areas of interest. His work in the fields of Image segmentation and Normalization overlaps with other areas such as Modal.

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.

Top Publications

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Bjoern H. Menze;Andras Jakab;Stefan Bauer;Jayashree Kalpathy-Cramer.
IEEE Transactions on Medical Imaging (2015)

1848 Citations

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

Konstantinos Kamnitsas;Christian Ledig;Virginia F.J. Newcombe;Joanna P. Simpson.
Medical Image Analysis (2017)

1459 Citations

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer.
Unknown Journal (2018)

685 Citations

ElasticFusion: Dense SLAM Without A Pose Graph

Thomas Whelan;Stefan Leutenegger;Renato F. Salas-Moreno;Ben Glocker.
robotics science and systems (2015)

553 Citations

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

Andrew I R Maas;David K Menon;P David Adelson;Nada Andelic.
Lancet Neurology (2017)

550 Citations

Dense image registration through MRFs and efficient linear programming.

Ben Glocker;Ben Glocker;Nikos Komodakis;Nikos Komodakis;Georgios Tziritas;Nassir Navab.
Medical Image Analysis (2008)

493 Citations

ElasticFusion: Real-time dense SLAM and light source estimation

Thomas Whelan;Renato F Salas-Moreno;Ben Glocker;Andrew J Davison.
The International Journal of Robotics Research (2016)

415 Citations

Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

K. Murphy;B. van Ginneken;J. M. Reinhardt;S. Kabus.
IEEE Transactions on Medical Imaging (2011)

401 Citations

Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images

Jamie Shotton;Ben Glocker;Christopher Zach;Shahram Izadi.
computer vision and pattern recognition (2013)

397 Citations

Attention U-Net: Learning Where to Look for the Pancreas

Ozan Oktay;Jo Schlemper;Loïc Le Folgoc;Matthew C. H. Lee.
arXiv: Computer Vision and Pattern Recognition (2018)

350 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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