Michael Aertsen

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 90 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 116 citations into it
• Artificial Intelligence — 63 citations into it
• Model Architectures — 26 citations into it
• Biophysics — 9 citations into it
• Media Technology — 8 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it

Whom Michael Aertsen trusts overall

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

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

Who trusts Michael Aertsen overall

1. Samuel Budd

Papers from Michael Aertsen's perspective

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

Their papers

• DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2017) — 476 citations
• Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks (2018) — 730 citations
• CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation (2020) — 655 citations
• Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning (2017) — 764 citations