Bennett A. Landman

Computer Vision and Pattern Recognition · 2007–2022

A researcher in the Science corpus: 16 papers; trusts 64 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 157 citations into it; #12,300 of 21,575 by trust here, #5,711 by citations
• Artificial Intelligence — 154 citations into it; #31,822 of 35,345 by trust here, #11,072 by citations
• Radiology, Nuclear Medicine and Imaging — 128 citations into it; #2,791 of 3,265 by trust here, #396 by citations
• Pediatrics, Perinatology and Child Health — 6 citations into it
• Orthopedics and Sports Medicine — 5 citations into it

Whom Bennett A. Landman trusts overall

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

1. Derek K. Jones
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Vasily L. Yarnykh
5. Stefan Skare
6. Kaiming He
7. Lin-Ching Chang
8. Ilya Sutskever
9. Mark Jenkinson
10. Yoshua Bengio

Who trusts Bennett A. Landman overall

1. Arvid Lundervold
2. Parnian Afshar

Papers from Bennett A. Landman's perspective

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

Their papers

• 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
• 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
• Federated learning enables big data for rare cancer boundary detection (2022) — 346 citations
• Metrics Reloaded: Recommendations for image analysis validation (2022) — 479 citations
• Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis (2021) — 876 citations
• 3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles (2019) — 296 citations
• Learning Implicit Brain MRI Manifolds with Deep Learning (2018) — 139 citations
• Fully Convolutional Neural Networks Improve Abdominal Organ Segmentation (2018) — 68 citations
• Deep Learning for Brain Tumor Classification (2017) — 329 citations
• Toward Content Based Image Retrieval with Deep Convolutional Neural Networks (2015) — 49 citations
• The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data (2014) — 790 citations
• Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group (2013) — 404 citations
• Multi-Parametric Neuroimaging Reproducibility: A 3T Resource Study (2010) — 318 citations
• Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T (2007) — 295 citations