Philippe Lambin

Radiology, Nuclear Medicine and Imaging · 2010–2021

A researcher in the Science corpus: 17 papers; trusts 11 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Radiology, Nuclear Medicine and Imaging — 104 citations into it; #686 of 3,265 by trust here, #129 by citations
• Artificial Intelligence — 96 citations into it; #35,335 of 35,345 by trust here, #8,365 by citations
• Molecular Biology — 58 citations into it
• Computer Vision and Pattern Recognition — 57 citations into it
• Oncology — 42 citations into it; #1,403 of 1,698 by trust here, #376 by citations
• Pulmonary and Respiratory Medicine — 42 citations into it; #151 of 770 by trust here, #84 by citations
• Evaluation & Benchmarks — 13 citations into it
• Health Informatics — 10 citations into it; #102 of 240 by trust here, #84 by citations

Whom Philippe Lambin trusts overall

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

1. L. Fass
2. Chintan Parmar
3. Hugo J. W. L. Aerts
4. Eran Segal
5. S. Formenti
6. Max Kuhn
7. S. Yip
8. Patrick Grossmann
9. Robert Tibshirani
10. S. Demaria

Who trusts Philippe Lambin overall

1. Chintan Parmar
2. Ahmed Hosny
3. I. E. El Naqa
4. Rikiya Yamashita
5. R. Larue
6. Hugo J. W. L. Aerts
7. Mathieu Hatt
8. D. Fehr

Papers from Philippe Lambin's perspective

Ordered by how strongly the researchers Philippe Lambin 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. Regression Shrinkage and Selection via the Lasso
4. Gradient-based learning applied to document recognition
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. An Introduction to Variable and Feature Selection
10. Controlling the false discovery rate: a practical and powerful approach to multiple testing

Their papers

• QIN “Radiomics: The Process and the Challenges” (2012) — 2,066 citations
• Radiomics: Extracting more information from medical images using advanced feature analysis (2012) — 5,387 citations
• Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods (2021) — 470 citations
• A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018) — 99 citations
• Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures (2017) — 321 citations
• Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries (2017) — 134 citations
• Radiomics: the bridge between medical imaging and personalized medicine (2017) — 4,912 citations
• The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. (2016) — 2,940 citations
• Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept. (2016) — 186 citations
• The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis (2015) — 409 citations
• CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. (2015) — 668 citations
• Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer (2015) — 381 citations
• Machine Learning methods for Quantitative Radiomic Biomarkers (2015) — 956 citations
• Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation (2014) — 567 citations
• Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (2014) — 4,696 citations
• Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (2014) — 741 citations
• The unfolded protein response protects human tumor cells during hypoxia through regulation of the autophagy genes MAP1LC3B and ATG5. (2010) — 816 citations