Ivana Isgum

Computer Vision and Pattern Recognition · 2009–2019

A researcher in the Science corpus: 10 papers; trusts 82 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 96 citations into it; #7,547 of 21,575 by trust here, #3,445 by citations
• Radiology, Nuclear Medicine and Imaging — 49 citations into it; #551 of 3,265 by trust here, #271 by citations
• Artificial Intelligence — 37 citations into it
• Optimization & Training — 16 citations into it
• Model Architectures — 15 citations into it
• Generative Models — 14 citations into it
• Computational Mechanics — 8 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Ivana Isgum trusts overall

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

1. K. Smith‐Miles
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yann LeCun
5. Alex Krizhevsky
6. Jimmy Ba
7. Yoshua Bengio
8. Torsten Rohlfing
9. Sergey Ioffe
10. Shun Miao

Who trusts Ivana Isgum overall

1. Juan Eugenio Iglesias
2. Antonio Criminisi
3. Mert Rory Sabuncu
4. Hongzhi Wang
5. Florin C. Ghesu
6. Xin Yi
7. Guha Balakrishnan
8. Amy Zhao

Papers from Ivana Isgum's perspective

Ordered by how strongly the researchers Ivana Isgum trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans (2009) — 429 citations
• Automatic Segmentation of MR Brain Images With a Convolutional Neural Network (2016) — 831 citations
• Generative Adversarial Networks for Noise Reduction in Low-Dose CT (2017) — 915 citations
• Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks (2017) — 424 citations
• State-of-the-Art Deep Learning in Cardiovascular Image Analysis. (2019) — 352 citations
• A deep learning framework for unsupervised affine and deformable image registration (2018) — 778 citations
• Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (2018) — 2,285 citations
• Deep MR to CT Synthesis Using Unpaired Data (2017) — 681 citations
• End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network (2017) — 485 citations
• Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities (2016) — 339 citations