D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 93 Citations 41,927 1,073 World Ranking 214 National Ranking 13

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Surgery

His main research concerns Artificial intelligence, Computer vision, Augmented reality, Segmentation and Pose. His research brings together the fields of Pattern recognition and Artificial intelligence. His Computer vision research includes themes of Computer graphics and Ultrasound.

His Augmented reality study also includes fields such as

  • Visualization which is related to area like Illusion,
  • Multimedia which connect with Mixed reality. He has researched Segmentation in several fields, including Labeled data and Benchmark. His research investigates the connection with Pose and areas like Video tracking which intersect with concerns in Tracking system.

His most cited work include:

  • V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation (2365 citations)
  • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. (982 citations)
  • Deeper Depth Prediction with Fully Convolutional Residual Networks (981 citations)

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

Nassir Navab mainly focuses on Artificial intelligence, Computer vision, Augmented reality, Pattern recognition and Segmentation. Many of his studies on Artificial intelligence apply to Machine learning as well. His studies deal with areas such as Imaging phantom, Ultrasound, Computer graphics and Robustness as well as Computer vision.

His Augmented reality study necessitates a more in-depth grasp of Human–computer interaction. His research is interdisciplinary, bridging the disciplines of Feature and Pattern recognition. Specifically, his work in Segmentation is concerned with the study of Image segmentation.

He most often published in these fields:

  • Artificial intelligence (82.98%)
  • Computer vision (57.52%)
  • Augmented reality (19.98%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (82.98%)
  • Computer vision (57.52%)
  • Machine learning (9.74%)

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

His primary areas of investigation include Artificial intelligence, Computer vision, Machine learning, Segmentation and Pattern recognition. His study in Deep learning, Convolutional neural network, Artificial neural network, Augmented reality and Medical imaging is carried out as part of his studies in Artificial intelligence. His Computer vision research includes elements of Imaging phantom and Visualization.

In his work, Graph is strongly intertwined with Graph, which is a subfield of Machine learning. A large part of his Segmentation studies is devoted to Image segmentation. Nassir Navab has included themes like Ground truth and Robustness in his Pattern recognition study.

Between 2018 and 2021, his most popular works were:

  • Staingan: Stain Style Transfer for Digital Histological Images (83 citations)
  • QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy. (83 citations)
  • QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy. (83 citations)

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

  • Artificial intelligence
  • Computer vision
  • Surgery

Nassir Navab focuses on Artificial intelligence, Computer vision, Deep learning, Segmentation and Pattern recognition. He combines subjects such as Machine learning and Graph with his study of Artificial intelligence. The various areas that he examines in his Computer vision study include Visualization and Benchmark.

The concepts of his Deep learning study are interwoven with issues in Embedding and Data mining. His biological study spans a wide range of topics, including Pixel and Medical imaging. In general Pattern recognition, his work in Training set is often linked to Transfer linking many areas of study.

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.

Best Publications

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Fausto Milletari;Nassir Navab;Seyed-Ahmad Ahmadi.
international conference on 3d vision (2016)

1722 Citations

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

Babak Ehteshami Bejnordi;Mitko Veta;Paul Johannes van Diest;Bram van Ginneken.
JAMA (2017)

846 Citations

Deeper Depth Prediction with Fully Convolutional Residual Networks

Iro Laina;Christian Rupprecht;Vasileios Belagiannis;Federico Tombari.
international conference on 3d vision (2016)

824 Citations

Tissue Classification as a Potential Approach for Attenuation Correction in Whole-Body PET/MRI: Evaluation with PET/CT Data

Axel Martinez-Möller;Michael Souvatzoglou;Gaspar Delso;Ralph A. Bundschuh.
The Journal of Nuclear Medicine (2009)

684 Citations

Model globally, match locally: Efficient and robust 3D object recognition

Bertram Drost;Markus Ulrich;Nassir Navab;Slobodan Ilic.
computer vision and pattern recognition (2010)

650 Citations

Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes

Stefan Hinterstoisser;Vincent Lepetit;Slobodan Ilic;Stefan Holzer.
asian conference on computer vision (2012)

588 Citations

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010

Tianzi Jiang;Nassir Navab;Josien P. W. Pluim;Max A. Viergever.
Lecture Notes in Computer Science (2010)

555 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

Gradient Response Maps for Real-Time Detection of Textureless Objects

S. Hinterstoisser;C. Cagniart;S. Ilic;P. Sturm.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)

414 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

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