Ender Konukoglu

Computer Vision and Pattern Recognition · 2006–2022

A researcher in the Science corpus: 15 papers; trusts 1,025 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 392 citations into it; #9,457 of 21,575 by trust here, #2,443 by citations
• Artificial Intelligence — 226 citations into it; #8,139 of 35,345 by trust here, #2,975 by citations
• Representation & Self-Supervised Learning — 95 citations into it
• Radiology, Nuclear Medicine and Imaging — 77 citations into it; #2,638 of 3,265 by trust here, #2,462 by citations
• Cognitive Neuroscience — 66 citations into it
• Model Architectures — 51 citations into it
• Neurology — 35 citations into it
• Molecular Biology — 34 citations into it

Whom Ender Konukoglu trusts overall

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

1. Bruce Fischl
2. Geoffrey E. Hinton
3. Trevor J. Hastie
4. Alex Krizhevsky
5. Kaiming He
6. Radford M. Neal
7. Diederik P. Kingma
8. Ross B. Girshick
9. Leo Breiman
10. Yoshua Bengio

Who trusts Ender Konukoglu overall

1. Christian F. Baumgartner
2. Lisa M. Koch
3. Gabriele Fanelli
4. Kerem Can Tezcan
5. Piotr Doll'ar
6. Matthias Dantone
7. Saurabh Gupta
8. Juergen Gall
9. Jia Xi Ang
10. Luc Van Gool

Papers from Ender Konukoglu's perspective

Ordered by how strongly the researchers Ender Konukoglu 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015) — 6,325 citations
• Regression Forests for Efficient Anatomy Detection and Localization in CT Studies (2010) — 326 citations
• Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR (2012) — 304 citations
• Exploring Cross-Image Pixel Contrast for Semantic Segmentation (2021) — 586 citations
• Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2012) — 967 citations
• Contrastive learning of global and local features for medical image segmentation with limited annotations (2020) — 697 citations
• Rethinking Semantic Segmentation: A Prototype View (2022) — 366 citations
• Visual Feature Attribution Using Wasserstein GANs (2017) — 158 citations
• Iterative Interaction Training for Segmentation Editing Networks (2018) — 36 citations
• Reconstructing subject‐specific effect maps (2017) — 4 citations
• SubCMap: Subject and Condition Specific Effect Maps (2017)
• Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases (2016)
• Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests (2015) — 21 citations
• Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2011) — 328 citations
• Shape-based hand recognition (2006) — 365 citations