Lubomir M. Hadjiiski

Computer Vision and Pattern Recognition · 2002–2020

A researcher in the Science corpus: 8 papers; trusts 194 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 118 citations into it; #20,281 of 21,575 by trust here, #14,151 by citations
• Artificial Intelligence — 111 citations into it; #33,028 of 35,345 by trust here, #11,750 by citations
• Radiology, Nuclear Medicine and Imaging — 81 citations into it; #2,398 of 3,265 by trust here, #934 by citations
• Model Architectures — 25 citations into it
• Optimization & Training — 19 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Safety, Robustness & Interpretability — 13 citations into it

Whom Lubomir M. Hadjiiski trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Yann LeCun
5. Ilya Sutskever
6. Kaiming He
7. Matthew D. Zeiler
8. Diederik P. Kingma
9. Christian Szegedy
10. Sergey Ioffe

Who trusts Lubomir M. Hadjiiski overall

1. Kenji Suzuki
2. Heang-Ping Chan
3. Sarmad Shafique
4. Yading Yuan

Papers from Lubomir M. Hadjiiski's perspective

Ordered by how strongly the researchers Lubomir M. Hadjiiski 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. (2016) — 247 citations
• Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI (2020) — 69 citations
• Computer-Aided Diagnosis in the Era of Deep Learning (2020) — 297 citations
• Deep learning in medical imaging and radiation therapy. (2018) — 668 citations
• Multi-task transfer learning deep convolutional neural network: Application to computer-aided diagnosis of breast cancer on mammograms (2017) — 203 citations
• Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. (2016) — 270 citations
• Medical Imaging 2015: Computer-Aided Diagnosis (2015) — 3 citations
• Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. (2002) — 365 citations