Nico Karssemeijer

Computer Vision and Pattern Recognition · 2011–2017

A researcher in the Science corpus: 9 papers; trusts 6 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 86 citations into it
• Artificial Intelligence — 65 citations into it; #34,401 of 35,345 by trust here, #3,384 by citations
• Optimization & Training — 23 citations into it
• Radiology, Nuclear Medicine and Imaging — 16 citations into it; #3,050 of 3,265 by trust here, #679 by citations
• Pulmonary and Respiratory Medicine — 14 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Nico Karssemeijer trusts overall

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

1. Chih-Jen Lin
2. Jerome H. Friedman
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Thomas M. Cover
6. Kaiming He
7. Yoshua Bengio

Who trusts Nico Karssemeijer overall

1. Geert J. S. Litjens

Papers from Nico Karssemeijer's perspective

Ordered by how strongly the researchers Nico Karssemeijer 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. Going deeper with convolutions
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer (2017) — 3,174 citations
• Stain Specific Standardization of Whole-Slide Histopathological Images (2016) — 294 citations
• Large scale deep learning for computer aided detection of mammographic lesions (2017) — 912 citations
• Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation (2017) — 391 citations
• Using deep learning to segment breast and fibroglandular tissue in MRI volumes (2017) — 204 citations
• Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring (2016) — 390 citations
• Computer-Aided Detection of Prostate Cancer in MRI (2014) — 467 citations
• Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis (2012) — 128 citations
• Ultrasonics, Ferroelectrics, and Frequency Control (2011) — 157 citations