Natalie N. C. Shih

Computer Vision and Pattern Recognition · 2012–2017

A researcher in the Science corpus: 4 papers; trusts 4 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 46 citations into it; #17,963 of 21,575 by trust here, #8,409 by citations
• Artificial Intelligence — 39 citations into it; #29,143 of 35,345 by trust here, #9,465 by citations
• Representation & Self-Supervised Learning — 9 citations into it

Whom Natalie N. C. Shih trusts overall

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

1. Yann LeCun
2. Yoshua Bengio
3. Serge Beucher
4. Corinna Cortes

Who trusts Natalie N. C. Shih overall

1. Anant Madabhushi
2. Andrew Janowczyk

Papers from Natalie N. C. Shih's perspective

Ordered by how strongly the researchers Natalie N. C. Shih trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Learning Multiple Layers of Features from Tiny Images
9. Building high-level features using large scale unsupervised learning
10. ImageNet: A large-scale hierarchical image database

Their papers

• Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks (2014) — 632 citations
• Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent (2017) — 479 citations
• Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features (2014) — 334 citations
• Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer (2012) — 105 citations