Ronald M. Summers

Computer Vision and Pattern Recognition · 2005–2020

A researcher in the Science corpus: 25 papers; trusts 1,061 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 481 citations into it; #8,381 of 21,575 by trust here, #1,538 by citations
• Artificial Intelligence — 389 citations into it; #10,765 of 35,345 by trust here, #3,498 by citations
• Radiology, Nuclear Medicine and Imaging — 220 citations into it; #208 of 3,265 by trust here, #21 by citations
• Model Architectures — 72 citations into it
• Evaluation & Benchmarks — 64 citations into it; #2,028 of 3,147 by trust here, #725 by citations
• Pulmonary and Respiratory Medicine — 64 citations into it; #63 of 770 by trust here, #21 by citations
• Biomedical Engineering — 60 citations into it; #3,484 of 10,734 by trust here, #5,210 by citations
• Natural Language Processing — 54 citations into it

Whom Ronald M. Summers trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Ilya Sutskever
6. Radford M. Neal
7. Yann LeCun
8. Karen Simonyan
9. Diederik P. Kingma
10. Ross B. Girshick

Who trusts Ronald M. Summers overall

1. G. Handelman
2. Xiaosong Wang
3. Maciej A. Mazurowski
4. Holger R. Roth
5. Le Lu
6. Michael Yeung
7. H. Kok
8. Pranav Rajpurkar
9. Mateusz Buda
10. Berkman Sahiner

Papers from Ronald M. Summers's perspective

Ordered by how strongly the researchers Ronald M. Summers trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning (2016) — 5,088 citations
• DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation (2015) — 870 citations
• Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation (2016) — 394 citations
• ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases (2019) — 1,263 citations
• TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays (2018) — 560 citations
• A large annotated medical image dataset for the development and evaluation of segmentation algorithms (2019) — 1,013 citations
• Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition (2017) — 41 citations
• NegBio: a high-performance tool for negation and uncertainty detection in radiology reports (2017) — 229 citations
• DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning (2018) — 562 citations
• Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆ (2017) — 300 citations
• Machine Learning and Radiology (2012) — 660 citations
• Preparing Medical Imaging Data for Machine Learning (2020) — 891 citations
• On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities. (2020) — 425 citations
• Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets (2020) — 535 citations
• A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020) — 1,021 citations
• Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks (2019) — 650 citations
• Deep learning in medical imaging and radiation therapy. (2018) — 668 citations
• DeepPap: Deep Convolutional Networks for Cervical Cell Classification (2017) — 431 citations
• ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases (2017) — 3,535 citations
• Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks (2017) — 66 citations