Bjoern H Menze

Computer Vision and Pattern Recognition · 2009–2022

A researcher in the Science corpus: 18 papers; trusts 59 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 181 citations into it
• Artificial Intelligence — 132 citations into it
• Statistics and Probability — 36 citations into it
• Model Architectures — 31 citations into it
• Evaluation & Benchmarks — 30 citations into it
• Optimization & Training — 15 citations into it
• Computational Theory and Mathematics — 12 citations into it
• Statistical Learning Theory — 12 citations into it

Whom Bjoern H Menze trusts overall

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

1. Leo Breiman
2. Trevor J. Hastie
3. Zhuowen Tu
4. Andy Liaw
5. Thomas G. Dietterich
6. Eric R. Ziegel
7. Yoav Freund
8. J. Platt
9. Tin Kam Ho
10. Mark R. Segal

Who trusts Bjoern H Menze overall

1. Fabian Isensee
2. Konstantinos Kamnitsas
3. Hao Chen
4. Ye Ren
5. Sahand Hariri
6. Rina Friedberg
7. M. Zia
8. S. Adelabu
9. Hannes Feilhauer
10. Guotai Wang

Papers from Bjoern H Menze's perspective

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

Their papers

• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015) — 6,325 citations
• On Oblique Random Forests (2011) — 256 citations
• The Liver Tumor Segmentation Benchmark (LiTS) (2019) — 1,500 citations
• The Medical Segmentation Decathlon (2021) — 1,513 citations
• A large annotated medical image dataset for the development and evaluation of segmentation algorithms (2019) — 1,013 citations
• A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data (2009) — 1,177 citations
• Why rankings of biomedical image analysis competitions should be interpreted with care (2018) — 393 citations
• Beyond Medical Imaging - A Review of Multimodal Deep Learning in Radiology (2022)
• Federated learning enables big data for rare cancer boundary detection (2022) — 346 citations
• Metrics Reloaded: Recommendations for image analysis validation (2022) — 479 citations
• The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification (2021) — 1,091 citations
• Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge (2021) — 467 citations
• clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation (2020) — 549 citations
• Machine learning analysis of whole mouse brain vasculature (2020) — 307 citations
• DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual Learning (2018) — 29 citations
• Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks (2017) — 365 citations
• Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields (2016) — 679 citations
• ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI (2016) — 525 citations