Michael Yeung
Computer Vision and Pattern Recognition · 2020–2021
A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 33 citations into it
- Radiology, Nuclear Medicine and Imaging — 31 citations into it
- Artificial Intelligence — 21 citations into it
- Biomedical Engineering — 6 citations into it
Whom Michael Yeung trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Shruti Jadon
- Xavier Glorot
- Olaf Ronneberger
- Yoshua Bengio
- Nabila Abraham
- Shijun Wang
- Vijay Badrinarayanan
- Fausto Milletarì
- William R. Crum
- Seyed Sadegh Mohseni Salehi
Papers from Michael Yeung's perspective
Ordered by how strongly the researchers Michael Yeung trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions
- Long Short-Term Memory
Their papers
- Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation (2021) — 662 citations
- Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans (2020) — 936 citations