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 57 Citations 21,007 336 World Ranking 1840 National Ranking 1010

Research.com Recognitions

Awards & Achievements

2019 - Fellow of the Indian National Academy of Engineering (INAE)

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Internal medicine
  • Radiology

Ronald M. Summers mainly investigates Artificial intelligence, Radiology, Segmentation, Pattern recognition and Computer vision. Ronald M. Summers regularly links together related areas like Machine learning in his Artificial intelligence studies. Ronald M. Summers works mostly in the field of Machine learning, limiting it down to topics relating to Image and, in certain cases, Recurrent neural network.

As part of one scientific family, Ronald M. Summers deals mainly with the area of Radiology, narrowing it down to issues related to the Colonoscopy, and often Lumen. When carried out as part of a general Segmentation research project, his work on Image segmentation and Sørensen–Dice coefficient is frequently linked to work in Prior probability, therefore connecting diverse disciplines of study. His work on Conditional random field and k-nearest neighbors algorithm as part of general Pattern recognition study is frequently linked to Gaussian blur, Digital subscriber line and Network architecture, therefore connecting diverse disciplines of science.

His most cited work include:

  • Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning (2315 citations)
  • ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases (1067 citations)
  • Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation (339 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, Pattern recognition, Segmentation and Computer vision. His biological study spans a wide range of topics, including Virtual colonoscopy and Machine learning. His study on Computer-aided diagnosis is often connected to Computer aided detection as part of broader study in Radiology.

His Computer-aided diagnosis study combines topics from a wide range of disciplines, such as Cancer and CAD. His research integrates issues of Artificial neural network, Image and Feature in his study of Pattern recognition. His Segmentation study incorporates themes from Lesion, Random forest and Computed tomography.

He most often published in these fields:

  • Artificial intelligence (49.46%)
  • Radiology (34.64%)
  • Pattern recognition (29.46%)

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

  • Artificial intelligence (49.46%)
  • Pattern recognition (29.46%)
  • Segmentation (25.00%)

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

His primary areas of study are Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Medical imaging. His Artificial intelligence research incorporates elements of Machine learning, Computed tomography and Natural language processing. The concepts of his Pattern recognition study are interwoven with issues in Domain, Artificial neural network and Surgical planning.

His studies in Segmentation integrate themes in fields like Lesion, Cylinder, Pixel, Translation and Radiology. His work in the fields of Radiology, such as Abdominal ct, overlaps with other areas such as Response Evaluation Criteria in Solid Tumors. The Medical imaging study combines topics in areas such as Semi-supervised learning, Image segmentation, Radiography, Anomaly detection and Data science.

Between 2018 and 2021, his most popular works were:

  • Deep learning in medical imaging and radiation therapy. (208 citations)
  • A large annotated medical image dataset for the development and evaluation of segmentation algorithms (203 citations)
  • Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. (85 citations)

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

  • Artificial intelligence
  • Internal medicine
  • Radiology

Ronald M. Summers spends much of his time researching Artificial intelligence, Pattern recognition, Segmentation, Medical imaging and Radiology. His Artificial intelligence study combines topics in areas such as Pneumonia and Lung disease. Ronald M. Summers combines subjects such as Feature, Deep neural networks, Regularization, Representation and Image with his study of Pattern recognition.

His Segmentation research includes themes of Pixel, Lesion detection and Computed tomography. His work deals with themes such as Semi-supervised learning, Training set, Artificial neural network, Field and Convolutional neural network, which intersect with Medical imaging. His work carried out in the field of Radiology brings together such families of science as Prostate, Prostate cancer, Multiparametric MRI and Cohort.

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

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Hoo-Chang Shin;Holger R. Roth;Mingchen Gao;Le Lu.
IEEE Transactions on Medical Imaging (2016)

2992 Citations

Polyps: Linear and Volumetric Measurement at CT Colonography

Srinath C. Yeshwant;Ronald M. Summers;Jianhua Yao;Daniel S. Brickman.
Radiology (2006)

1874 Citations

Polyp Size Measurement at CT Colonography: What Do We Know and What Do We Need to Know?

Ronald M. Summers.
Radiology (2010)

1865 Citations

ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

Xiaosong Wang;Yifan Peng;Le Lu;Zhiyong Lu.
computer vision and pattern recognition (2017)

1249 Citations

Machine learning and radiology

Shijun Wang;Ronald M. Summers.
Medical Image Analysis (2012)

481 Citations

Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation

Holger R. Roth;Le Lu;Jiamin Liu;Jianhua Yao.
IEEE Transactions on Medical Imaging (2016)

446 Citations

Automated polyp detection at CT colonography: feasibility assessment in a human population.

Ronald M. Summers;C. Daniel Johnson;Lynne M. Pusanik;James D. Malley.
Radiology (2001)

350 Citations

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Holger R. Roth;Le Lu;Amal Farag;Hoo-Chang Shin.
medical image computing and computer assisted intervention (2015)

342 Citations

Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population.

Ronald M. Summers;Jianhua Yao;Perry J. Pickhardt;Marek Franaszek.
Gastroenterology (2005)

336 Citations

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Amber L. Simpson;Michela Antonelli;Spyridon Bakas;Michel Bilello.
arXiv: Computer Vision and Pattern Recognition (2019)

301 Citations

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

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