Fabian Isensee

Computer Vision and Pattern Recognition · 2016–2024

A researcher in the Science corpus: 17 papers; trusts 393 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 252 citations into it; #10,877 of 21,575 by trust here, #3,659 by citations
• Artificial Intelligence — 170 citations into it
• Radiology, Nuclear Medicine and Imaging — 130 citations into it; #3,099 of 3,265 by trust here, #417 by citations
• Model Architectures — 73 citations into it
• Biomedical Engineering — 58 citations into it; #8,282 of 10,734 by trust here, #3,269 by citations
• Neurology — 45 citations into it
• Optimization & Training — 38 citations into it
• Biophysics — 34 citations into it; #946 of 1,196 by trust here, #287 by citations

Whom Fabian Isensee trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Olaf Ronneberger
4. X. Zhang
5. Sergey Ioffe
6. Christian Szegedy
7. Ross B. Girshick
8. Geoffrey E. Hinton
9. Alex Krizhevsky
10. Shaoqing Ren

Who trusts Fabian Isensee overall

1. Jun Ma
2. Junde Wu
3. Paul F. Jaeger
4. Reza Azad
5. Ali Hatamizadeh
6. Yin Dai
7. Tongxue Zhou
8. Guotai Wang
9. Klaus Hermann Maier-Hein
10. Michael Yeung

Papers from Fabian Isensee's perspective

Ordered by how strongly the researchers Fabian Isensee 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
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

• 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
• The Medical Segmentation Decathlon (2021) — 1,513 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
• nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation (2024) — 438 citations
• Metrics Reloaded: Recommendations for image analysis validation (2022) — 479 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
• Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (2018) — 2,285 citations
• Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge (2017) — 538 citations
• The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. (2016) — 2,940 citations