Thema: SNSF VISIBLE - HES SO Valais Publications

Arthur Chevalley, Valentin Oreiller, Julien Fageot, John O. Prior, Vincent Andrearczyk und Adrien Depeursinge, A Bispectral 3D UNet for Rotation Robustness in Medical Segmentation, in: The First Workshop on Topology- and Graph-Informed Imaging Informatics at MICCAI, Seiten 43-54, Springer Nature Switzerland, 2024

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Sacha Bors, Daniel Abler, Matthieu Dietz, Vincent Andrearczyk, Julien Fageot, Marie Nicod-Lalonde, Niklaus Schaefer, Robert DeKemp, Christel H. Kamani, John O. Prior und Adrien Depeursinge, Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction (2024), in: Nature Scientific Reports, 14:9644

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Vincent Andrearczyk, Valentin Oreiller, Sarah Boughdad, Catherine Cheze-Le-Rest, Olena Tankyevych, Hesham Elhalawani, Mario Jreige, John O. Prior, Martin Vallìères, Dimitris Visvikis, Mathieu Hatt und Adrien Depeursinge, 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)

[]: IF 2022: 10.9

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Daphné Faist, Mario Jreige, Valentin Oreiller, Marie Nicod-Lalonde, Niklaus Schaefer, Adrien Depeursinge und John O. Prior, 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)

[]: IF 2022: 1.7

Vincent Andrearczyk, Valentin Oreiller, Mario Jreige, Hesham Elhalawani, Sarah Boughdad, Catherine Cheze-Le-Rest, John O. Prior, Mathieu Hatt und Adrien Depeursinge, HEad and neCK TumOR segmentation and outcome prediction: The HECKTOR challenge, in: European Society of Radiology, 2022

Head and Neck Tumor Segmentation and Outcome Prediction, Springer International Publishing, 2022

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Philip Whybra, Roger Schaer, Adrien Depeursinge, Vincent Andrearczyk, Henning Müller, Martin Vallìères und Alex Zwanenburg, The Image Biomarker Standardisation Initiative (IBSI) On Reproducible Convolutional Radiomics, in: European Society of Radiology, 2022

Vincent Andrearczyk, Valentin Oreiller, Sarah Boughdad, Catherine Cheze-Le-Rest, Hesham Elhalawani, Mario Jreige, John O. Prior, Martin Vallìères, Dimitris Visvikis, Mathieu Hatt und Adrien Depeursinge, 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, Seiten 1-37, 2022

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Vincent Andrearczyk, Valentin Oreiller, Mario Jreige, Joel Castelli, John O. Prior und Adrien Depeursinge, 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), Seiten 4731-4735, 2022

Valentin Oreiller, Julien Fageot, Vincent Andrearczyk, John O. Prior und Adrien Depeursinge, Multi-Organ Nucleus Segmentation Using a Locally Rotation Invariant Bispectral U-Net, in: Medical Imaging with Deep Learning, 2022

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Virginie Uhlmann, Zsuzsanna Püspöki, Adrien Depeursinge, Michael Unser, Daniel Sage und Julien Fageot, Steer'n'Detect: Fast 2D Template Detection with Accurate Orientation Estimation (2022), in: Bioinformatics, 38:11(3146–3148)

[]: IF 2019 = 5.61
[]: Demo page: http://bigwww.epfl.ch/algorithms/steer_n_detect/
[]: Source code: https://github.com/Biomedical-Imaging-Group/Steer-n-Detect
[]: Zenodo: https://zenodo.org/records/10078907

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Daniel Abler, Vincent Andrearczyk, Valentin Oreiller, Javier Barranco Garcia, Diem Vuong, Stephanie Tanadini-Lang, Matthias Guckenberger, Mauricio Reyes und Adrien Depeursinge, 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, Seiten 367–380, Springer International Publishing, 2022

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Pierre Fontaine, Vincent Andrearczyk, Valentin Oreiller, Daniel Abler, Joel Castelli, Oscar Acosta, R. De Crevoisier, Martin Vallìères, Mario Jreige, John O. Prior und Adrien Depeursinge, 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)

[]: IF 2021 = 4.739

Daniel Abler, Roger Schaer, Valentin Oreiller, Himanshu Verma, Julien Reichenbach, Orfeas Aidonopoulos, Florian Evéquoz, Mario Jreige, John O. Prior und Adrien Depeursinge, QuantImage v2: A Comprehensive and Integrated Physician-Centered Cloud Platform for Radiomics and Machine Learning Research (2023), in: European Radiology Experimental, 7:16

[]: IF 2022: 3.75

Roger Schaer, Valentin Oreiller, Daniel Abler, Himanshu Verma, Julien Reichenbach, Florian Evéquoz, Mario Jreige, John O. Prior und Adrien Depeursinge, QuantImage v2: A Clinician-in-the-loop Cloud Platform for Radiomics Research, in: European Society of Radiology, 2022

Valentin Oreiller, Vincent Andrearczyk, Mario Jreige, Sarah Boughdad, Hesham Elhalawani, Joel Castelli, Martin Vallìères, Simeng Zhu, Juanying Xie, Ying Peng, Andrei Iantsen, Mathieu Hatt, Yading Yuan, Jun Ma, Xiaoping Yang, Chinmay Rao, Suraj Pai, Kanchan Ghimire, Xue Feng, Mohamed A. Naser, Clifton D. Fuller, Fereshteh Yousefirizi, Arman Rahmim, Huai Chen, Lisheng Wang, John O. Prior und Adrien Depeursinge, Head and Neck Tumor Segmentation in PET/CT: The HECKTOR Challenge (2022), in: Medical Image Analysis, 77(102336)

[]: IF 2020 = 8.545

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Vincent Andrearczyk, Pierre Fontaine, Valentin Oreiller, Joel Castelli, Mario Jreige, John O. Prior und Adrien Depeursinge, Multi-Task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer, in: 4th Workshop on PRedictive Intelligence in MEdicine, Seiten 147-156, Springer LNCS, 2021

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Pierre Fontaine, Vincent Andrearczyk, Valentin Oreiller, Joel Castelli, Mario Jreige, John O. Prior und Adrien Depeursinge, Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT, in: Multimodal Learning for Clinical Decision Support, Seiten 59-68, Springer LNCS, 2021

Himanshu Verma, Jakub Mlynar, Roger Schaer, Julien Reichenbach, Mario Jreige, John O. Prior, Florian Evéquoz und Adrien Depeursinge, Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows, in: ACM CHI 2023, 2023

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Oscar Jimenez del Toro, Christoph Aberle, Michael Bach, Roger Schaer, Markus Obmann, Kyriakos Flouris, Ender Konukoglu, Bram Stieltjes, Henning Müller und Adrien Depeursinge, The discriminative power and stability of radiomics features with CT variations: Task-based analysis in an anthropomorphic 3D-printed CT phantom (2021), in: Investigative Radiology, 56:12(820-825)

[]: IF 2020 = 5.156

Mara Graziani, Thomas Lompech, Henning Müller, Adrien Depeursinge und Vincent Andrearczyk, On the Scale Invariance in State of the Art CNNs Trained on ImageNet (2021), in: Special Issue "Interpretable and Annotation-Efficient Learning for Medical Image Computing" in Machine Learning and Knowledge Extraction:3(374–391)

Roger Schaer, Valentin Oreiller, Orfeas Aidonopoulos, John O. Prior und Adrien Depeursinge, QuantImage v2: an Open-Source and Web-Based Integrated Platform for Clinical Radiomics Research, in: Joint scientific session SSRMP/SGR-SSR, 2021

Vincent Andrearczyk, Valentin Oreiller, Mario Jreige, Martin Vallìères, Hesham Elhalawani, Sarah Boughdad, John O. Prior und Adrien Depeursinge, Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT, Seiten 1-21, 2021

Oscar Jimenez del Toro, Christoph Aberle, Michael Bach, Roger Schaer, Markus Obmann, Kyriakos Flouris, Ender Konukoglu, Bram Stieltjes, Adrien Depeursinge und Henning Müller, Revealing most suitable CT radiomics features for retrospective studies with heterogeneous datasets, in: European Congress of Radiology (ECR) 2021, ONLINE edition, 2021

Pierre Fontaine, Oscar Acosta, Joel Castelli, R. De Crevoisier, Henning Müller und Adrien Depeursinge, The Importance of Feature Aggregation in Radiomics: A Head and Neck Cancer Study (2020), in: Nature Scientific Reports, 10:19679

[]: IF 2019 = 3.998

Mara Graziani, Thomas Lompech, Henning Müller, Adrien Depeursinge und Vincent Andrearczyk, Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging, in: Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2020, 2020

Vincent Andrearczyk, Valentin Oreiller und Adrien Depeursinge, Oropharynx Detection in PET-CT for Tumor Segmentation, in: Irish Machine Vision and Image Processing Conference, 2020, 2020

Vincent Andrearczyk, Mara Graziani, Henning Müller und Adrien Depeursinge, Consistency of Scale Covariance in Internal Representations of CNNs, in: Irish Machine Vision and Image Processing Conference, 2020

Adrien Depeursinge, Vincent Andrearczyk, Philip Whybra, Joost van Griethuysen, Henning Müller, Roger Schaer, Martin Vallìères und Alex Zwanenburg, Standardised convolutional filtering for radiomics, 2020

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Valentin Oreiller, Vincent Andrearczyk, Julien Fageot, John O. Prior und Adrien Depeursinge, 3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis, 2020

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Gwenaelle Creff, Anne Devillers, Adrien Depeursinge, Xavier Palard-Novello, Oscar Acosta, Franck Jegoux und Joel Castelli, Evaluation of the Prognostic Value of FDG PET/CT Parameters for Patients with Surgically Treated Head and Neck Cancer: A Systematic Review (2020), in: JAMA Otolaryngology - Head and Neck Surgery, 146:5(471-479)

[]: IF 2019 = 3.295

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Vincent Andrearczyk, Valentin Oreiller, Martin Vallìères, Joel Castelli, Hesham Elhalawani, Mario Jreige, Sarah Boughdad, John O. Prior und Adrien Depeursinge, 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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Vincent Andrearczyk, Julien Fageot, Valentin Oreiller, Xavier Montet und Adrien Depeursinge, Local Rotation Invariance in 3D CNNs (2020), in: Medical Image Analysis, 65(101756)

[]: IF 2018 = 8.79

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Vincent Andrearczyk, Valentin Oreiller, Julien Fageot, Xavier Montet und Adrien Depeursinge, Solid Spherical Energy (SSE) CNNs for Efficient 3D Medical Image Analysis, in: Irish Machine Vision and Image Processing Conference, Seiten 37-44, 2019

Roberto Gatta, Adrien Depeursinge, Osman Ratib, Olivier Michielin und Antoine Leimgruber, Integrating radiomics into holomics for personalised oncology: from algorithms to bedside (2020), in: European Radiology Experimental, 4(11)

Julien Fageot, Virginie Uhlmann, Adrien Depeursinge, Zsuzsanna Püspöki, Daniel Sage und Michael Unser, Radial B-Splines for Optimal Detection in Images, in: ISBI Special Session on Spline Models in Biomedical Imaging, 2019

Vincent Andrearczyk, Julien Fageot, Valentin Oreiller, Xavier Montet und Adrien Depeursinge, Exploring local rotation invariance in 3D CNNs with steerable filters, in: Medical Imaging with Deep Learning, Seiten 15-26, Proceedings of Machine Learning Research, 2019

[]: Won the overall best paper award of MIDL 2019 !

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Guillaume Vanoost, Yashin Dicente Cid, Daniel L. Rubin und Adrien Depeursinge, A lung graph model for the classification of interstitial lung disease on CT images, in: SPIE Medical Imaging 2019: Computer-Aided Diagnosis, International Society for Optics and Photonics, Seiten 869-876, SPIE, 2019

Joel Castelli, Adrien Depeursinge, Anne Devillers, B. Campillo-Gimenez, Yashin Dicente Cid, John O. Prior, Enrique Chajon, Franck Jegoux, C. Sire, Oscar Acosta, X. Sun, B. De Bari, J. Bourhis und R. De Crevoisier, PET-based predictive survival model after radiotherapy for head and neck cancer (2019), in: European Journal of Nuclear Medicine and Molecular Imaging, 46:3(638-649)

[]: IF 2016 = 7.277

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Michael T. McCann, Vincent Andrearczyk, Michael Unser und Adrien Depeursinge, Fast Rotational Sparse Coding (2018)(arXiv:1806.04374)

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Vincent Andrearczyk, Adrien Depeursinge und Henning Müller, Learning Cross-Protocol Radiomics and Deep Feature Standardization from CT Images of Texture Phantoms, in: SPIE Medical Imaging 2019, International Society for Optics and Photonics, Seiten 109-116, SPIE, 2019

Vincent Andrearczyk und Adrien Depeursinge, Rotational 3D Texture Classification Using Group Equivariant CNNs (2018), in: ArXiv

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Adrien Depeursinge, Julien Fageot, Vincent Andrearczyk, John-Paul Ward und Michael Unser, Rotation Invariance and Directional Sensitivity: Spherical Harmonics versus Radiomics Features, in: Machine Learning in Medical Imaging (MLMI), Seiten 107--115, Springer International Publishing, 2018

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Ranveer Joyseeree, Sebastian Otalora, Henning Müller und Adrien Depeursinge, Fusing Learned Representations from Riesz and Deep CNNs for Lung Tissue Classification (2019), in: Medical Image Analysis, 56(172-183)

[]: IF 2017 = 5.356

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Vincent Andrearczyk, Adrien Depeursinge und Henning Müller, Neural Network Training for Cross-Protocol Radiomic Feature Standardization in Computed Tomography (2019), in: Journal of Medical Imaging, 6:3(024008)

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Dmitry Chezerov, Dmitry Goldgof, Lawrence Hall, Robert Gillies, Matther Schabath, Henning Müller und Adrien Depeursinge, Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness (2019), in: Nature Scientific Reports, 9:1(4500)

[]: IF 2016 = 4.259

Anaïs Badoual, Michael Unser und Adrien Depeursinge, Texture-Driven Parametric Snakes for Semi-Automatic Image Segmentation (2019), in: Computer Vision and Image Understanding, 188(102793)

[]: IF 2018 = 2.645
[]: Icy plugin and source code: https://bigwww.epfl.ch/demo/texture-snake/

Julien Fageot, Virginie Uhlmann, Zsuzsanna Püspöki, Benjamin Beck, Michael Unser und Adrien Depeursinge, Principled Design and Implementation of Steerable Detectors (2021), in: IEEE Transactions on Image Processing, 30(4465-4478)

[]: IF 2020 = 9.34
[]: Demo page: http://bigwww.epfl.ch/algorithms/steer_n_detect/
[]: Source code: https://github.com/Biomedical-Imaging-Group/Steer-n-Detect

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Adrien Depeursinge, Tunde Szilágyi, Yan Liu, Kazmer Kovacs, Reena Thomas, Kristen Yeom, Nancy Fischbein, Daniel L. Rubin, Michael Iv und Olivier Gevaert, Locoregional radiogenomic models to capture gene expression heterogeneity in glioblastoma (2018), in: biorXiv

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