Mauricio Reyes

Computer Vision and Pattern Recognition · 2011–2022

A researcher in the Science corpus: 9 papers; trusts 26 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 122 citations into it; #10,498 of 21,575 by trust here, #2,729 by citations
• Artificial Intelligence — 105 citations into it
• Biophysics — 27 citations into it; #855 of 1,196 by trust here, #880 by citations
• Evaluation & Benchmarks — 11 citations into it
• Media Technology — 11 citations into it
• Model Architectures — 11 citations into it
• Optimization & Training — 9 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Mauricio Reyes trusts overall

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

1. Christopher K. I. Williams
2. Geoffrey E. Hinton
3. Pang Wei Koh
4. Kaiming He
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Chih-Jen Lin
8. Karen Simonyan
9. Yoshua Bengio
10. Yann LeCun

Who trusts Mauricio Reyes overall

1. Waddah Saeed
2. Bas H. M. van der Velden
3. A. Işın
4. Sérgio Pereira
5. Christian Walter Peter Omlin
6. Andriy Myronenko
7. Evgin Göçeri

Papers from Mauricio Reyes's perspective

Ordered by how strongly the researchers Mauricio Reyes 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet Large Scale Visual Recognition Challenge
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015) — 6,325 citations
• Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization (2011) — 388 citations
• A survey of MRI-based medical image analysis for brain tumor studies (2013) — 995 citations
• Federated learning enables big data for rare cancer boundary detection (2022) — 346 citations
• Metrics Reloaded: Recommendations for image analysis validation (2022) — 479 citations
• On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities. (2020) — 425 citations
• On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation (2018) — 124 citations
• Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (2018) — 2,033 citations
• Understanding and Interpreting Machine Learning in Medical Image Computing Applications (2018) — 82 citations