Trefwoorden:
Publicaties van Valentin Oreiller gesorteerd op titel
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, , , en , 3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis, 2020
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| , , , , en , A Bispectral 3D UNet for Rotation Robustness in Medical Segmentation, in: The First Workshop on Topology- and Graph-Informed Imaging Informatics at MICCAI, pagina's 43-54, Springer Nature Switzerland, 2024 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , en , A Multimodal and Multi-centric Head and Neck Cancer Dataset for Tumor Segmentation and Outcome Prediction (2025) |
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| , , , , , en , An overview of self-supervised deep learning applications to molecular data (2026), in: Briefings in Bioinformatics, 27:4(bbag380) |
, , , , , , , , , , en , Automatic Head and Neck Tumor Segmentation and Outcome Prediction Relying on FDG-PET/CT Images: Findings from the Second Edition of the HECKTOR Challenge (2023), in: Medical Image Analysis, 90:1(102972)
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| , , , , , , , en , Automatic Segmentation of Head and Neck Tumors and Nodal Metastases in PET-CT scans, in: Medical Imaging with Deep Learning, Montréal, Canada, 2020 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , en , Biomedical image analysis competitions: The state of current participation practice (2022), in: arXiv |
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, , , , , , , , , en , Cleaning Radiotherapy Contours for Radiomics Studies, is it Worth it? A Head and Neck Cancer Study (2022), in: Clinical and Translational Radiation Oncology, 33(153-158)
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| , , , , , , , en , Comparison of MR preprocessing strategies and sequences for radiomics-based MGMT prediction, in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (MICCAI/BrainLes 2021), Cham, pagina's 367–380, Springer International Publishing, 2022 |
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, , , en , Exploring local rotation invariance in 3D CNNs with steerable filters, in: Medical Imaging with Deep Learning, pagina's 15-26, Proceedings of Machine Learning Research, 2019
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| , , , , , en , Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT, in: Multimodal Learning for Clinical Decision Support, pagina's 59-68, Springer LNCS, 2021 |
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| Head and Neck Tumor Segmentation, Springer International Publishing, 2021 |
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| Head and Neck Tumor Segmentation and Outcome Prediction, Springer International Publishing, 2022 |
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| , , en , Head and Neck Tumor Segmentation and Outcome Prediction, Springer International Publishing, 2023 |
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| , , , , , , en , HEad and neCK TumOR segmentation and outcome prediction using AI: lessons from three consecutive years of the HECKTOR challenge, in: European Head and Neck Society (EHNS) on Artificial Intelligence (AI) in Head & Neck Oncology, Lausanne and virtual, 2023 |
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| , , , , , , , en , HEad and neCK TumOR segmentation and outcome prediction: The HECKTOR challenge, in: European Society of Radiology, 2022 |
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, , , , , , , , , , , , , , , , , , , , , , , , , en , Head and Neck Tumor Segmentation in PET/CT: The HECKTOR Challenge (2022), in: Medical Image Analysis, 77(102336)
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| , , , , , , en , Impact of a Gaussian filter applied to post-reconstruction PET images on radiomic features to predict complete pathological response in breast cancer (2020), in: Journal of Nuclear Medicine, 61:supplement 1(606--606) |
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| , , , , , , en , Impact of a Gaussian filter applied to post-reconstruction PET on radiomic features in assessing tumor heterogeneity in breast cancer. (2020), in: Journal of Nuclear Medicine, 61:supplement 1(612--612) |
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, , , en , Local Rotation Invariance in 3D CNNs (2020), in: Medical Image Analysis, 65(101756)
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| , , , en , Multi-Organ Nucleus Segmentation Using a Locally Rotation Invariant Bispectral U-Net, in: Medical Imaging with Deep Learning, 2022 |
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| , , , , , en , Multi-Task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer, in: 4th Workshop on PRedictive Intelligence in MEdicine, pagina's 147-156, Springer LNCS, 2021 |
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| , en , Oropharynx Detection in PET-CT for Tumor Segmentation, in: Irish Machine Vision and Image Processing Conference, 2020, 2020 |
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| , , , , , , en , Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT, pagina's 1-21, 2021 |
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| , , , , , , , , , en , Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images, in: Head and Neck Tumor Segmentation and Outcome Prediction, pagina's 1-37, 2022 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , en , Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT, pagina's 1-30, Springer, Cham, 2023 |
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| , , en , PET/CT Radiomics Analysis Contributes to Detection of Pulmonary Lymphangitic Carcinomatosis (PLC) in Non-Small Cell Lung Cancer (NSCLC), in: Swiss Congress of Radiology, 2019 |
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| , , , , en , PET/CT Radiomics predict Pulmonary Lymphangitic Carcinomatosis (PLC) in Non-Small Cell Lung Cancer (NSCLC) (2020), in: Journal of Nuclear Medicine, 61:supplement 1(1311--1311) |
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| , , , , , , , en , QuantImage v2: A Clinician-in-the-loop Cloud Platform for Radiomics Research, in: European Society of Radiology, 2022 |
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, , , , , , , , en , QuantImage v2: A Comprehensive and Integrated Physician-Centered Cloud Platform for Radiomics and Machine Learning Research (2023), in: European Radiology Experimental, 7:16
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| , , , en , QuantImage v2: an Open-Source and Web-Based Integrated Platform for Clinical Radiomics Research, in: Joint scientific session SSRMP/SGR-SSR, 2021 |
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| , , , , , en , Reproducibility of lung cancer radiomic features extracted from data-driven respiratory gating and free-breathing flow imaging in 18F-FDG PET/CT, in: 2022 Annual Meeting of the Society of Nuclear Medicine and Molecular Imaging (SNMMI), 2022 |
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, , , , , en , Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [18F]-FDG PET/CT (2022), in: European Journal of Hybrid Imaging, 6:1(33)
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| , , , , en , Segmentation and Classification of Head and Neck Nodal Metastases and Primary Tumors in PET/CT, in: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pagina's 4731-4735, 2022 |
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| , , , en , Solid Spherical Energy (SSE) CNNs for Efficient 3D Medical Image Analysis, in: Irish Machine Vision and Image Processing Conference, pagina's 37-44, 2019 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , en , Why is the winner the best?, in: CVPR, 2023 |
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| , en , Wide kernels and their DCT compression in convolutional networks for nuclei segmentation (2023), in: Informatics in Medicine Unlocked, 43(101403) |
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