Hoo-Chang Shin

Computer Vision and Pattern Recognition · 2013–2022

A researcher in the Science corpus: 9 papers; trusts 563 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 201 citations into it; #7,760 of 21,575 by trust here, #1,211 by citations
• Artificial Intelligence — 158 citations into it; #18,133 of 35,345 by trust here, #2,747 by citations
• Natural Language Processing — 55 citations into it
• Representation & Self-Supervised Learning — 34 citations into it; #2,033 of 3,145 by trust here, #1,671 by citations
• Model Architectures — 29 citations into it
• Media Technology — 16 citations into it
• Optimization & Training — 16 citations into it
• Multimodal Learning — 14 citations into it

Whom Hoo-Chang Shin trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Ilya Sutskever
6. Karen Simonyan
7. David G. Lowe
8. Diederik P. Kingma
9. Kaiming He
10. Ross B. Girshick

Who trusts Hoo-Chang Shin overall

1. Dinggang Shen
2. Junwei Han
3. Heung-Il Suk
4. B. van Ginneken
5. Dingwen Zhang
6. Angela Zhang
7. Berkman Sahiner
8. Xiaosong Wang
9. Chaopeng Shen
10. Mohammad Mustafa Taye

Papers from Hoo-Chang Shin's perspective

Ordered by how strongly the researchers Hoo-Chang Shin 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015) — 6,325 citations
• Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning (2016) — 5,088 citations
• DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation (2015) — 870 citations
• Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation (2016) — 394 citations
• Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data (2013) — 498 citations
• Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition (2017) — 41 citations
• A large language model for electronic health records (2022) — 857 citations
• Bio-Megatron: Larger Biomedical Domain Language Model (2020) — 177 citations
• Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks (2018) — 676 citations