Le Lu

Computer Vision and Pattern Recognition · 2014–2022

A researcher in the Science corpus: 18 papers; trusts 1,008 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 344 citations into it; #8,376 of 21,575 by trust here, #1,454 by citations
• Artificial Intelligence — 230 citations into it; #17,396 of 35,345 by trust here, #4,829 by citations
• Model Architectures — 64 citations into it
• Radiology, Nuclear Medicine and Imaging — 61 citations into it; #262 of 3,265 by trust here, #24 by citations
• Evaluation & Benchmarks — 50 citations into it; #1,903 of 3,147 by trust here, #725 by citations
• Natural Language Processing — 46 citations into it
• Multimodal Learning — 42 citations into it; #1,077 of 1,211 by trust here, #846 by citations
• Representation & Self-Supervised Learning — 35 citations into it

Whom Le Lu trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Kaiming He
5. Yann LeCun
6. Ilya Sutskever
7. Yoshua Bengio
8. Ross B. Girshick
9. David G. Lowe
10. Andrew Zisserman

Who trusts Le Lu overall

1. Xiaosong Wang
2. Holger R. Roth
3. Qi-Yan Dou
4. Hao Chen
5. Berkman Sahiner
6. Maciej A. Mazurowski
7. Lequan Yu
8. B. van Ginneken
9. Pichao Wang
10. Hayit Greenspan

Papers from Le Lu's perspective

Ordered by how strongly the researchers Le Lu 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation (2021) — 5,990 citations
• 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
• 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
• LViT: Language Meets Vision Transformer in Medical Image Segmentation (2022) — 318 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
• Deep Learning and Convolutional Neural Networks for Medical Image Computing (2017) — 204 citations
• Deep convolutional networks for automated detection of posterior-element fractures on spine CT (2016) — 58 citations
• Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation (2015) — 585 citations
• A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations (2014) — 580 citations