Heang-Ping Chan

Artificial Intelligence · 2002–2020

A researcher in the Science corpus: 7 papers; trusts 97 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #25,515 of 35,345 by trust here, #10,798 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #20,273 of 21,575 by trust here, #14,151 by citations
• Radiology, Nuclear Medicine and Imaging — 26 citations into it; #1,873 of 3,265 by trust here, #814 by citations
• Optimization & Training — 13 citations into it
• Model Architectures — 12 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Safety, Robustness & Interpretability — 8 citations into it
• Biophysics — 5 citations into it

Whom Heang-Ping Chan trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Ilya Sutskever
5. Yann LeCun
6. Kaiming He
7. Sergey Ioffe
8. Matthew D. Zeiler
9. Diederik P. Kingma
10. Christian Szegedy

Who trusts Heang-Ping Chan overall

1. Kenji Suzuki
2. Berkman Sahiner
3. Lubomir M. Hadjiiski
4. Aria Pezeshk
5. Maryellen Lissak Giger

Papers from Heang-Ping Chan's perspective

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

Their papers

• Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. (2016) — 247 citations
• Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI (2020) — 69 citations
• Computer-Aided Diagnosis in the Era of Deep Learning (2020) — 297 citations
• Multi-task transfer learning deep convolutional neural network: Application to computer-aided diagnosis of breast cancer on mammograms (2017) — 203 citations
• Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. (2016) — 270 citations
• Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. (2008) — 361 citations
• Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. (2002) — 365 citations