Jiang Liu
Computer Vision and Pattern Recognition · IEEE Transactions on Medical Imaging · 2010–2019
A researcher in the Science corpus: 8 papers; trusts 102 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 96 citations into it
- Radiology, Nuclear Medicine and Imaging — 58 citations into it; #1,599 of 3,265 by trust here, #610 by citations
- Ophthalmology — 53 citations into it; #89 of 222 by trust here, #21 by citations
- Artificial Intelligence — 32 citations into it
- Model Architectures — 11 citations into it
- Media Technology — 9 citations into it
- Medical Imaging — 8 citations into it
- Biophysics — 6 citations into it
Whom Jiang Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Saining Xie
- Ross B. Girshick
- Yann LeCun
- Shaoqing Ren
- Diederik P. Kingma
Who trusts Jiang Liu overall
Papers from Jiang Liu's perspective
Ordered by how strongly the researchers Jiang Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- CE-Net: Context Encoder Network for 2D Medical Image Segmentation (2019) — 2,080 citations
- Uniqueness-Driven Saliency Analysis for Automated Lesion Detection with Applications to Retinal Diseases (2018) — 28 citations
- Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation (2018) — 836 citations
- Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image (2018) — 404 citations
- DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field (2016) — 462 citations
- Glaucoma detection based on deep convolutional neural network (2015) — 383 citations
- Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening (2013) — 613 citations
- ORIGA-light: An online retinal fundus image database for glaucoma analysis and research (2010) — 556 citations