Ozan Oktay

Computer Vision and Pattern Recognition · 2016–2023

A researcher in the Science corpus: 12 papers; trusts 263 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 215 citations into it
• Artificial Intelligence — 151 citations into it
• Model Architectures — 49 citations into it
• Natural Language Processing — 29 citations into it
• Biomedical Engineering — 28 citations into it
• Media Technology — 20 citations into it
• Biophysics — 8 citations into it
• Computational Mechanics — 6 citations into it

Whom Ozan Oktay trusts overall

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

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

Who trusts Ozan Oktay overall

1. Md Mostafijur Rahman
2. Jieneng Chen
3. Yundong Zhang
4. Nahian Alam Siddique
5. Chen Chen
6. Xiangde Luo
7. Nabila Abraham
8. Ademola Enitan Ilesanmi
9. Chenxin Li
10. Guotai Wang

Papers from Ozan Oktay's perspective

Ordered by how strongly the researchers Ozan Oktay 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks (2016) — 410 citations
• Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation (2017) — 431 citations
• Attention U-Net: Learning Where to Look for the Pancreas (2018) — 7,627 citations
• Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images (2018) — 1,840 citations
• Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing (2022) — 489 citations
• Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation (2017) — 732 citations
• Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing (2023) — 304 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
• Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling (2018) — 77 citations
• Automated cardiovascular magnetic resonance image analysis with fully convolutional networks (2017) — 706 citations
• Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography (2016) — 104 citations