Klaus Hermann Maier-Hein

Computer Vision and Pattern Recognition · 2013–2024

A researcher in the Science corpus: 27 papers; trusts 460 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 350 citations into it; #10,744 of 21,575 by trust here, #3,361 by citations
• Artificial Intelligence — 271 citations into it
• Radiology, Nuclear Medicine and Imaging — 219 citations into it; #1,923 of 3,265 by trust here, #295 by citations
• Model Architectures — 91 citations into it
• Biomedical Engineering — 70 citations into it; #6,937 of 10,734 by trust here, #2,647 by citations
• Neurology — 55 citations into it
• Biophysics — 51 citations into it; #947 of 1,196 by trust here, #287 by citations
• Optimization & Training — 50 citations into it

Whom Klaus Hermann Maier-Hein trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Olaf Ronneberger
5. Alex Krizhevsky
6. Ross B. Girshick
7. Yoshua Bengio
8. X. Zhang
9. Christian Szegedy
10. Jimmy Ba

Who trusts Klaus Hermann Maier-Hein overall

1. Arvid Lundervold
2. Fabian Isensee
3. Jun Ma
4. Junde Wu
5. Reza Azad

Papers from Klaus Hermann Maier-Hein's perspective

Ordered by how strongly the researchers Klaus Hermann Maier-Hein 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. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• A Probabilistic U-Net for Segmentation of Ambiguous Images (2018) — 731 citations
• nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation (2020) — 9,406 citations
• nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation (2018) — 1,100 citations
• Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection (2018) — 233 citations
• CHAOS Challenge - Combined (CT-MR) Healthy Abdominal Organ Segmentation (2020) — 882 citations
• MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation (2023) — 419 citations
• The future of digital health with federated learning (2020) — 2,960 citations
• Why rankings of biomedical image analysis competitions should be interpreted with care (2018) — 393 citations
• nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation (2024) — 438 citations
• Federated learning enables big data for rare cancer boundary detection (2022) — 346 citations
• Metrics Reloaded: Recommendations for image analysis validation (2022) — 479 citations
• MONAI: An open-source framework for deep learning in healthcare (2022) — 996 citations
• Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge (2021) — 467 citations
• nnU-Net for Brain Tumor Segmentation (2020) — 440 citations
• Automated brain extraction of multisequence MRI using artificial neural networks (2019) — 710 citations
• The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge (2019) — 625 citations
• Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. (2019) — 375 citations
• Automated Design of Deep Learning Methods for Biomedical Image Segmentation (2019) — 325 citations
• No New-Net (2018) — 444 citations
• TractSeg ‐ Fast and accurate white matter tract segmentation (2018) — 625 citations