Dong Ni

Computer Vision and Pattern Recognition · Engineering · 2014–2020

A researcher in the Science corpus: 5 papers; trusts 25 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 63 citations into it
• Artificial Intelligence — 54 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Dong Ni trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Pascal Vincent
5. L. V. D. van der Maaten
6. Yann LeCun
7. Ilya Sutskever
8. Vladimir Vezhnevets
9. Hugo Larochelle
10. Kaiming He

Who trusts Dong Ni overall

1. Mufti Mahmud
2. Christian F. Baumgartner
3. Geert J. S. Litjens
4. M. Bakator
5. Yu-An Chung

Papers from Dong Ni's perspective

Ordered by how strongly the researchers Dong Ni 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. Long Short-Term Memory
4. ImageNet: A large-scale hierarchical image database
5. ImageNet Large Scale Visual Recognition Challenge
6. Gradient-based learning applied to document recognition
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Attention is All you Need (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans (2016) — 730 citations
• A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging (2020) — 478 citations
• Deep Learning in Medical Ultrasound Analysis: A Review (2019) — 723 citations
• Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks (2015) — 348 citations
• Automatic Vaginal Bacteria Segmentation and Classification Based on Superpixel and Deep Learning (2014) — 7 citations