Michael J. Fulham

Computer Vision and Pattern Recognition · 2013–2019

A researcher in the Science corpus: 7 papers; trusts 183 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 67 citations into it
• Artificial Intelligence — 50 citations into it
• Radiology, Nuclear Medicine and Imaging — 29 citations into it
• Pulmonary and Respiratory Medicine — 17 citations into it
• Model Architectures — 13 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Optimization & Training — 12 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Michael J. Fulham trusts overall

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

1. Yoshua Bengio
2. Alex Krizhevsky
3. Kaiming He
4. Geoffrey E. Hinton
5. David G. Lowe
6. Radford M. Neal
7. Karen Simonyan
8. Ilya Sutskever
9. Andrea Vedaldi
10. Jia Deng

Papers from Michael J. Fulham's perspective

Ordered by how strongly the researchers Michael J. Fulham 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. ImageNet Large Scale Visual Recognition Challenge
7. Going deeper with convolutions
8. Attention is All you Need (Neural Information Processing Systems)
9. Long Short-Term Memory
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Multi-Modal Neuroimaging Feature Learning for Multi-Class Diagnosis of Alzheimer’s Disease (2014) — 524 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
• Adapted Treatment Guided by Interim PET-CT Scan in Advanced Hodgkin’s Lymphoma (2016) — 713 citations
• A Visual Analytics Approach Using the Exploration of Multidimensional Feature Spaces for Content-Based Medical Image Retrieval (2015) — 35 citations
• Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data (2013) — 231 citations