I. E. El Naqa
Radiology, Nuclear Medicine and Imaging · 2010–2021
A researcher in the Science corpus: 11 papers; trusts 27 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Radiology, Nuclear Medicine and Imaging — 91 citations into it; #664 of 3,265 by trust here, #1,216 by citations
- Artificial Intelligence — 53 citations into it
- Pulmonary and Respiratory Medicine — 45 citations into it
- Computer Vision and Pattern Recognition — 43 citations into it
- Molecular Biology — 31 citations into it
- Biomedical Engineering — 29 citations into it; #2,220 of 10,734 by trust here, #10,266 by citations
- Statistics and Probability — 20 citations into it
- Radiation — 16 citations into it
Whom I. E. El Naqa trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Issam El-Naqa
- John Shawe-Taylor
- James A. Hanley
- Habib Zaidi
- Bernhard Schölkopf
- James A. Sethian
- Yongyi Yang
- Nello Cristianini
- Trevor J. Hastie
- Koji Tsuda
Who trusts I. E. El Naqa overall
Papers from I. E. El Naqa's perspective
Ordered by how strongly the researchers I. E. El Naqa trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- GENERATIVE ADVERSARIAL NETS
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Human-level control through deep reinforcement learning
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- The role of quantitative PET in predicting cancer treatment outcomes (2014) — 60 citations
- Liver metastasis restrains immunotherapy efficacy via macrophage-mediated T cell elimination (2021) — 859 citations
- Machine and Deep Learning Methods for Radiomics (2020) — 517 citations
- Unraveling Biophysical Interactions of Radiation Pneumonitis in Non-Small-Cell Lung Cancer via Bayesian Network Analysis (2017) — 57 citations
- Deep Reinforcement Learning for Automated Radiation Adaptation in Lung Cancer (2017) — 241 citations
- Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries (2017) — 134 citations
- Radiogenomics and Radiotherapy Response Modeling (2017) — 52 citations
- Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer (2017) — 488 citations
- The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. (2016) — 2,940 citations
- Chapter 16: Practical reinforcement learning in dynamic treatment regimes (2015) — 3 citations
- PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques (2010) — 392 citations