Philippe Lambin
Orcid: 0000-0001-7961-0191
According to our database1,
Philippe Lambin
authored at least 31 papers
between 2007 and 2024.
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Bibliography
2024
Improving shared decision making for lung cancer treatment by developing and validating an open-source web based patient decision aid for stage I-II non-small cell lung cancer.
Frontiers Digit. Health, 2024
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review.
CoRR, 2024
Counterfactuals and Uncertainty-Based Explainable Paradigm for the Automated Detection and Segmentation of Renal Cysts in Computed Tomography Images: A Multi-Center Study.
CoRR, 2024
Methodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial.
CoRR, 2024
A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging.
CoRR, 2024
2023
MSCDA: Multi-level semantic-guided contrast improves unsupervised domain adaptation for breast MRI segmentation in small datasets.
Neural Networks, August, 2023
Comput. Biol. Medicine, July, 2023
Precision-medicine-toolbox: An open-source python package for the quantitative medical image analysis.
Softw. Impacts, May, 2023
Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images.
J. Imaging, 2023
UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images.
IEEE Access, 2023
Leveraging Uncertainty Estimation for Segmentation of Kidney, Kidney Tumor and Kidney Cysts.
Proceedings of the Kidney and Kidney Tumor Segmentation - MICCAI 2023 Challenge, 2023
2022
Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.
Inf. Fusion, 2022
Precision-medicine-toolbox: An open-source python package for facilitation of quantitative medical imaging and radiomics analysis.
CoRR, 2022
Transparency of deep neural networks for medical image analysis: A review of interpretability methods.
Comput. Biol. Medicine, 2022
HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG- PET/CT Images.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2022
2021
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging.
CoRR, 2021
Privacy preserving distributed learning classifiers - Sequential learning with small sets of data.
Comput. Biol. Medicine, 2021
Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging.
Comput. Biol. Medicine, 2021
2020
Combining hypoxia-activated prodrugs and radiotherapy in silico: Impact of treatment scheduling and the intra-tumoural oxygen landscape.
PLoS Comput. Biol., 2020
CoRR, 2020
Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM).
IEEE Access, 2020
2019
Development and validation of a patient decision aid for prostate Cancer therapy: from paternalistic towards participative shared decision making.
BMC Medical Informatics Decis. Mak., 2019
2018
Multi-Scale Modeling and Oxygen Impact on Tumor Temporal Evolution: Application on Rectal Cancer During Radiotherapy.
IEEE Trans. Medical Imaging, 2018
2015
Proceedings of the Digital Healthcare Empowering Europeans, 2015
2014
Ensemble analyses improve signatures of tumour hypoxia and reveal inter-platform differences.
BMC Bioinform., 2014
2013
Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach.
Pattern Recognit., 2013
2011
The Combination of Clinical, Dose-Related and Imaging Features Helps Predict Radiation-Induced Normal-Tissue Toxicity in Lung-cancer Patients - An in-silico Trial Using Machine Learning Techniques.
Proceedings of the 10th International Conference on Machine Learning and Applications and Workshops, 2011
2009
Survival Prediction in Lung Cancer Treated with Radiotherapy: Bayesian Networks vs. Support Vector Machines in Handling Missing Data.
Proceedings of the International Conference on Machine Learning and Applications, 2009
2008
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008
2007
Proceedings of the Advances in Neural Information Processing Systems 20, 2007
Reducing a Biomarkers List via Mathematical Programming: Application to Gene Signatures to Detect Time-Dependent Hypoxia in Cancer.
Proceedings of the Sixth International Conference on Machine Learning and Applications, 2007