Bernhard Kainz

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 14 papers; trusts 316 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 182 citations into it; #20,108 of 21,575 by trust here, #3,089 by citations
• Artificial Intelligence — 134 citations into it
• Radiology, Nuclear Medicine and Imaging — 65 citations into it; #2,782 of 3,265 by trust here, #410 by citations
• Model Architectures — 43 citations into it
• Media Technology — 16 citations into it
• Medical Imaging — 14 citations into it
• Biophysics — 13 citations into it
• Aerospace Engineering — 12 citations into it

Whom Bernhard Kainz trusts overall

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

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

Who trusts Bernhard Kainz overall

1. Arvid Lundervold

Papers from Bernhard Kainz's perspective

Ordered by how strongly the researchers Bernhard Kainz 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks (2016) — 410 citations
• Attention U-Net: Learning Where to Look for the Pancreas (2018) — 7,627 citations
• Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images (2018) — 1,840 citations
• Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation (2017) — 732 citations
• Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection (2019) — 181 citations
• A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis (2019) — 617 citations
• Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents (2018) — 61 citations
• Automatic Shadow Detection in 2D Ultrasound (2018) — 5 citations
• Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation (2017) — 460 citations
• Automated cardiovascular magnetic resonance image analysis with fully convolutional networks (2017) — 706 citations
• DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images (2017) — 85 citations
• SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound (2016) — 388 citations
• Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks (2016) — 69 citations
• Learning clinically useful information from images: Past, present and future (2016) — 33 citations