Christian Wachinger

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 8 papers; trusts 328 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 95 citations into it; #9,181 of 21,575 by trust here, #7,421 by citations
• Artificial Intelligence — 72 citations into it; #16,180 of 35,345 by trust here, #12,580 by citations
• Radiology, Nuclear Medicine and Imaging — 27 citations into it; #2,127 of 3,265 by trust here, #1,355 by citations
• Model Architectures — 23 citations into it; #1,594 of 2,278 by trust here, #1,774 by citations
• Optimization & Training — 21 citations into it
• Biomedical Engineering — 11 citations into it
• Biophysics — 10 citations into it
• Media Technology — 9 citations into it

Whom Christian Wachinger trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Yoshua Bengio
5. Ilya Sutskever
6. Sergey Ioffe
7. Kaiming He
8. Xavier Glorot
9. Yann LeCun
10. Christian Szegedy

Who trusts Christian Wachinger overall

1. Seyed Sadegh Mohseni Salehi
2. Nima Tajbakhsh
3. José Bernal
4. Deniz Erdoğmuş
5. Abhijit Guha Roy
6. Valerio Marsocci
7. Laura Jeyaseelan
8. Maithra Raghu
9. Kaisar Kushibar

Papers from Christian Wachinger's perspective

Ordered by how strongly the researchers Christian Wachinger trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness (2019) — 22 citations
• The Medical Segmentation Decathlon (2021) — 1,513 citations
• ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network (2017) — 577 citations
• Importance Driven Continual Learning for Segmentation Across Domains (2020) — 36 citations
• BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning (2019) — 359 citations
• Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks (2018) — 1,025 citations
• Recalibrating Fully Convolutional Networks With Spatial and Channel “Squeeze and Excitation” Blocks (2018) — 467 citations
• DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy (2017) — 353 citations