Jianming Liang

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 7 papers; trusts 281 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 101 citations into it; #4,307 of 21,575 by trust here, #3,280 by citations
• Artificial Intelligence — 60 citations into it
• Model Architectures — 23 citations into it
• Oncology — 19 citations into it; #162 of 1,698 by trust here, #923 by citations
• Media Technology — 8 citations into it
• Biophysics — 5 citations into it

Whom Jianming Liang trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ross B. Girshick
6. Evan Shelhamer
7. Yann LeCun
8. Ilya Sutskever
9. X. Zhang
10. Karen Simonyan

Who trusts Jianming Liang overall

1. Deng-Ping Fan
2. Tao Zhou
3. Yundong Zhang
4. Xiaoqi Zhao
5. Md Mostafijur Rahman
6. B. Dong
7. Debesh Jha
8. Ge-Peng Ji
9. Sheng Fang
10. Ange Lou

Papers from Jianming Liang's perspective

Ordered by how strongly the researchers Jianming Liang 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. ImageNet: A large-scale hierarchical image database
4. Going deeper with convolutions
5. ImageNet Large Scale Visual Recognition Challenge
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Attention is All you Need (Neural Information Processing Systems)
9. Long Short-Term Memory
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• UNet++: A Nested U-Net Architecture for Medical Image Segmentation (2018) — 9,204 citations
• Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge (2017) — 414 citations
• Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information (2016) — 1,154 citations
• UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation (2019) — 3,790 citations
• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? (2016) — 2,955 citations
• Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis (2019) — 406 citations
• Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally (2017) — 409 citations