Marcel Prastawa

Computer Vision and Pattern Recognition · 2004–2018

A researcher in the Science corpus: 5 papers; trusts 6 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 77 citations into it; #12,931 of 21,575 by trust here, #2,530 by citations
• Artificial Intelligence — 43 citations into it
• Radiology, Nuclear Medicine and Imaging — 38 citations into it
• Neurology — 20 citations into it; #552 of 1,455 by trust here, #50 by citations
• Statistics and Probability — 9 citations into it

Whom Marcel Prastawa trusts overall

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

1. Peter J. Rousseeuw
2. Chris A. Cocosco
3. Eric R. Ziegel
4. Alex P. Zijdenbos
5. Guido Gerig
6. Katrien van Driessen
7. John G. Sled
8. Alan C. Evans
9. Martin Andreas Styner
10. Bernhard Schölkopf

Who trusts Marcel Prastawa overall

1. Charu C. Aggarwal
2. Mariano Cabezas
3. Ender Konukoglu
4. Rebecca C. Knickmeyer
5. Ben Glocker
6. Dinggang Shen
7. Konstantinos Kamnitsas
8. Brad Davis
9. Arnau Oliver
10. Antonios Makropoulos

Papers from Marcel Prastawa's perspective

Ordered by how strongly the researchers Marcel Prastawa 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. ImageNet: A large-scale hierarchical image database
5. Going deeper with convolutions
6. ImageNet Large Scale Visual Recognition Challenge
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
9. Rethinking the Inception Architecture for Computer Vision
10. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)

Their papers

• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015) — 6,325 citations
• A brain tumor segmentation framework based on outlier detection (2004) — 709 citations
• Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (2018) — 2,033 citations
• BACH: Grand challenge on breast cancer histology images (2018) — 716 citations
• Automatic segmentation of MR images of the developing newborn brain (2005) — 373 citations