D. Feng

Computer Vision and Pattern Recognition · IEEE Transactions on Biomedical Engineering · 2003–2021

A researcher in the Science corpus: 10 papers; trusts 333 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 122 citations into it; #14,025 of 21,575 by trust here, #6,954 by citations
• Artificial Intelligence — 84 citations into it
• Radiology, Nuclear Medicine and Imaging — 31 citations into it; #2,367 of 3,265 by trust here, #712 by citations
• Model Architectures — 28 citations into it
• Optimization & Training — 19 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Pulmonary and Respiratory Medicine — 16 citations into it
• Evaluation & Benchmarks — 9 citations into it

Whom D. Feng trusts overall

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

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

Who trusts D. Feng overall

1. Jong Chul Ye
2. Ge Wang
3. Ick Hoon Jang
4. Nam Chul Kim
5. Young Deok Chun

Papers from D. Feng's perspective

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

Their papers

• Accelerating magnetic resonance imaging via deep learning (2016) — 888 citations
• Medical image classification with convolutional neural network (2014) — 740 citations
• Multi-Modal Neuroimaging Feature Learning for Multi-Class Diagnosis of Alzheimer’s Disease (2014) — 524 citations
• EAPT: Efficient Attention Pyramid Transformer for Image Processing (2021) — 480 citations
• Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT (2019) — 439 citations
• Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks (2017) — 301 citations
• An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification (2017) — 473 citations
• Early diagnosis of Alzheimer's disease with deep learning (2014) — 489 citations
• Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data (2013) — 231 citations
• Fundamentals of Content-Based Image Retrieval (2003) — 494 citations