Konstantinos Kamnitsas

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 15 papers; trusts 492 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 179 citations into it; #7,437 of 21,575 by trust here, #2,304 by citations
• Artificial Intelligence — 137 citations into it; #12,649 of 35,345 by trust here, #3,220 by citations
• Model Architectures — 38 citations into it
• Representation & Self-Supervised Learning — 38 citations into it
• Optimization & Training — 24 citations into it
• Media Technology — 18 citations into it
• Biomedical Engineering — 16 citations into it; #4,306 of 10,734 by trust here, #1,387 by citations
• Biophysics — 11 citations into it

Whom Konstantinos Kamnitsas trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Karen Simonyan
5. Yoshua Bengio
6. Ilya Sutskever
7. Yann LeCun
8. Sergey Ioffe
9. Diederik P. Kingma
10. Andrew Zisserman

Who trusts Konstantinos Kamnitsas overall

1. Sergey I. Nikolenko
2. Andriy Myronenko
3. Fabian Isensee
4. Guotai Wang
5. Qi-Yan Dou
6. Tongxue Zhou
7. Wenqi Li
8. Jose Dolz
9. Egor Krivov
10. Ozan Oktay

Papers from Konstantinos Kamnitsas's perspective

Ordered by how strongly the researchers Konstantinos Kamnitsas 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation (2016) — 3,360 citations
• Domain Generalization via Model-Agnostic Learning of Semantic Features (2019) — 812 citations
• DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks (2016) — 410 citations
• Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation (2017) — 732 citations
• Federated learning enables big data for rare cancer boundary detection (2022) — 346 citations
• Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection (2019) — 181 citations
• Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents (2018) — 61 citations
• Autofocus Layer for Semantic Segmentation (2018) — 126 citations
• Towards continual learning in medical imaging (2018) — 63 citations
• Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research (2017) — 2,023 citations
• Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation (2017) — 460 citations
• SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound (2016) — 388 citations
• DeepMedic for Brain Tumor Segmentation (2016) — 325 citations
• Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks (2016) — 69 citations
• Unsupervised domain adaptation in brain lesion segmentation with adversarial networks (2016) — 544 citations