Olivier Gevaert

Artificial Intelligence · 2006–2023

A researcher in the Science corpus: 10 papers; trusts 65 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it; #11,265 of 35,345 by trust here, #16,771 by citations
• Molecular Biology — 66 citations into it
• Computer Vision and Pattern Recognition — 55 citations into it; #9,408 of 21,575 by trust here, #15,955 by citations
• Radiology, Nuclear Medicine and Imaging — 52 citations into it; #548 of 3,265 by trust here, #1,444 by citations
• Cancer Research — 45 citations into it; #589 of 1,780 by trust here, #1,486 by citations
• Optimization & Training — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Large Language Models — 6 citations into it

Whom Olivier Gevaert trusts overall

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

1. Radford M. Neal
2. Geoffrey E. Hinton
3. Yann LeCun
4. Richard E. Neapolitan
5. Yoshua Bengio
6. Alex Krizhevsky
7. A. Esquela-Kerscher
8. Olga G. Troyanskaya
9. L. V. D. van der Maaten
10. Ilya Sutskever

Who trusts Olivier Gevaert overall

1. Anika Cheerla
2. Verónica Bolón-Canedo
3. Wei Zhang
4. Sören Richard Stahlschmidt
5. Guang Yang
6. B. Bhinder
7. Noelia Sánchez-Maroño

Papers from Olivier Gevaert's perspective

Ordered by how strongly the researchers Olivier Gevaert 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Going deeper with convolutions
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation (2018) — 1,865 citations
• Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks (2006) — 371 citations
• Multimodal data fusion for cancer biomarker discovery with deep learning (2023) — 368 citations
• Disparities in dermatology AI performance on a diverse, curated clinical image set (2021) — 463 citations
• Deep learning with multimodal representation for pancancer prognosis prediction (2019) — 363 citations
• Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning (2019) — 369 citations
• Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas (2018)
• MicroRNA based Pan-Cancer Diagnosis and Treatment Recommendation (2017) — 67 citations
• Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation (2017) — 470 citations
• Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors and Machine-learning Approaches (2017) — 346 citations