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 70 Citations 28,613 231 World Ranking 801 National Ranking 6
Medicine D-index 75 Citations 18,542 266 World Ranking 11959 National Ranking 390

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Internal medicine
  • Radiology

Bram van Ginneken mainly investigates Artificial intelligence, Segmentation, Radiology, Computer vision and Computed tomography. Bram van Ginneken regularly ties together related areas like Machine learning in his Artificial intelligence studies. His Segmentation study integrates concerns from other disciplines, such as Pixel and Active appearance model.

His work carried out in the field of Radiology brings together such families of science as Lung and Receiver operating characteristic. Bram van Ginneken interconnects False positive paradox, Set, Support vector machine and Database in the investigation of issues within Computer vision. His studies in Computed tomography integrate themes in fields like Algorithm, Malignancy and Data set.

His most cited work include:

  • A survey on deep learning in medical image analysis (4326 citations)
  • Reflectance and texture of real-world surfaces (1079 citations)
  • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. (982 citations)

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

The scientist’s investigation covers issues in Artificial intelligence, Radiology, Segmentation, Pattern recognition and Computer vision. His research links Computed tomography with Artificial intelligence. His Radiology study combines topics in areas such as Lung cancer, Lung and Nuclear medicine.

His Segmentation research includes themes of Artificial neural network, Tomography and Image processing, Image. He has included themes like Contextual image classification, Image registration and Radiography in his Pattern recognition study. His Computer vision course of study focuses on Retina and Fundus.

He most often published in these fields:

  • Artificial intelligence (50.36%)
  • Radiology (32.78%)
  • Segmentation (28.03%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (50.36%)
  • Pattern recognition (23.99%)
  • Segmentation (28.03%)

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

Bram van Ginneken focuses on Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Radiology. He works mostly in the field of Artificial intelligence, limiting it down to concerns involving Computer vision and, occasionally, Field. His work on Training set is typically connected to Metric as part of general Pattern recognition study, connecting several disciplines of science.

His Segmentation research is multidisciplinary, relying on both Algorithm and Image, Similarity. His study focuses on the intersection of Deep learning and fields such as Inpainting with connections in the field of Encoder. His research in Radiology intersects with topics in Lung cancer, Lung and Receiver operating characteristic.

Between 2017 and 2021, his most popular works were:

  • CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation. (216 citations)
  • A large annotated medical image dataset for the development and evaluation of segmentation algorithms (203 citations)
  • The Liver Tumor Segmentation Benchmark (LiTS) (133 citations)

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

  • Artificial intelligence
  • Internal medicine
  • Radiology

Bram van Ginneken mostly deals with Radiology, Artificial intelligence, Segmentation, Lung cancer and Receiver operating characteristic. Radiology connects with themes related to Lung in his study. His Deep learning and Convolutional neural network study in the realm of Artificial intelligence connects with subjects such as Kappa.

Bram van Ginneken interconnects Interactive visualization, Field, Image and Labeled data in the investigation of issues within Segmentation. His research integrates issues of Calcification, Thoracic aorta, Cohort and Computed tomography in his study of Lung cancer. His Receiver operating characteristic research is multidisciplinary, incorporating elements of Radiological weapon, Stroma, Radiography and Confidence interval.

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

A survey on deep learning in medical image analysis

Geert J. S. Litjens;Thijs Kooi;Babak Ehteshami Bejnordi;Arnaud Arindra Adiyoso Setio.
Medical Image Analysis (2017)

5592 Citations

Reflectance and texture of real-world surfaces

Kristin J. Dana;Bram van Ginneken;Shree K. Nayar;Jan J. Koenderink.
ACM Transactions on Graphics (1999)

1781 Citations

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

Hayit Greenspan;Bram van Ginneken;Ronald M. Summers.
IEEE Transactions on Medical Imaging (2016)

1218 Citations

Comparative study of retinal vessel segmentation methods on a new publicly available database

Meindert Niemeijer;Meindert Niemeijer;Joes Staal;Bram van Ginneken;Marco Loog.
Medical Imaging 2004: Image Processing (2004)

928 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

Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

Arnaud Arindra Adiyoso Setio;Francesco Ciompi;Geert Litjens;Paul Gerke.
IEEE Transactions on Medical Imaging (2016)

786 Citations

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

Geert Litjens;Clara I. Sánchez;Nadya Timofeeva;Meyke Hermsen.
Scientific Reports (2016)

639 Citations

Large scale deep learning for computer aided detection of mammographic lesions

Thijs Kooi;Geert J. S. Litjens;Bram van Ginneken;Albert Gubern-Mérida.
Medical Image Analysis (2017)

593 Citations

Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs

Meindert Niemeijer;Bram van Ginneken;Michael J Cree;Atsushi Mizutani.
IEEE Transactions on Medical Imaging (2010)

496 Citations

Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database.

Bram van Ginneken;Mikkel Bille Stegmann;Marco Loog.
Medical Image Analysis (2006)

483 Citations

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