Vincent Andrearczyk - HES SO Valais Publications - Aigaion 2.0

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Primeiro nome(s):	Vincent
Sobrenome(s):	Andrearczyk
Instituto:	II

| 1-50 | 51-96 |

Mara Graziani, Thomas Lompech, Henning Müller and Vincent Andrearczyk, Evaluation and Comparison of CNN Visual Explanations for Histopathology, in: XAI-AAAI-21, 2021

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Mara Graziani, Thomas Lompech, Henning Müller, Adrien Depeursinge and 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)

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Mara Graziani, Thomas Lompech, Henning Müller, Adrien Depeursinge and 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

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Mara Graziani, Henning Müller and Vincent Andrearczyk, Concept attribution: Explaining CNN decisions to physicians (2020), in: Computers in Biology and Medicine

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Mara Graziani, Henning Müller and Vincent Andrearczyk, Interpreting intentionally flawed models with linear probes, in: ICCV workshop on statistical deep learning in computer vision, Seoul, Korea, 2019

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Mara Graziani, An-Phi Nguyen, Laura O' Mahony, Henning Müller and Vincent Andrearczyk, Concept discovery and Dataset exploration with Singular Value Decomposition, ICLR Workshop on Trustworthy ML, 2023

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Mara Graziani, Laura O' Mahony, An-Phi Nguyen, Henning Müller and Vincent Andrearczyk, Uncovering Unique Concept Vectors through Latent Space Decomposition

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Mara Graziani, Sebastian Otalora, Stéphane Marchand-Maillet, Henning Müller and Vincent Andrearczyk, Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization (2023), in: Machine Learning for Biomedical Imaging (MELBA), 2

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Mara Graziani, Sebastian Otalora, Henning Müller and Vincent Andrearczyk, Guiding CNNs towards Relevant Concepts by Multi-task and Adversarial Learning, arxiv, 2020

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Mara Graziani, Iam Palatnik de Sousa, Marley M Vellasco BR, Eduardo Costa da Silva, Henning Müller and Vincent Andrearczyk, Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability, in: MICCAI 2021, Springer, 2021

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Katharina Hoebel, Vincent Andrearczyk, Andrew Beers, Jay Patel, Ken Chang, Adrien Depeursinge, Henning Müller and Jayashree Kalpathy-Cramer, An Exploration of Uncertainty Information for Segmentation Quality Assessment, in: SPIE Medical Imaging 2020: Image Processing, Houston, TX, USA, páginas 381-390, SPIE, 2020

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Bogdan Ionescu, Henning Müller, Alba García Seco de Herrera, Vincent Andrearczyk, Yashin Dicente Cid, Vitali Liauchuk, Vassili Kovalev, Sadid A. Hasan, Yuan Ling, Oladimeji Farri, Joey Liu, Matthew Lungren, Duc-Tien Dang-Nguyen, Luca Piras, Michael Riegler, Liting Zhou, Mathias Lux, Cathal Gurrin, Adrian Clarck, Jon Chamberlain, Konstantinos Karampidis, Nikos Vasillopoulos, Carlos Roberto del Blanco, Carlos Cuevas Rodriguez, Ergina Kavallieratou and Narciso Garcia, ImageCLEF 2019: Multimedia Retrieval in Medical Nature, Security and Lifelogging Applications, in: ECIR 2019, Cologne, Germany, 2019

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Bogdan Ionescu, Henning Müller, Mauricio Villegas, Alba García Seco de Herrera, Carsten Eickhoff, Vincent Andrearczyk, Yashin Dicente Cid, Vitali Liauchuk, Vassili Kovalev, Sadid A. Hasan, Yuan Ling, Oladimeji Farri, Joey Liu, Matthew Lungren, Duc-Tien Dang-Nguyen, Luca Piras, Michael Riegler, Liting Zhou, Mathias Lux and Cathal Gurrin, Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation, in: CLEF conference proceeding, Avignon, France, Springer, 2018

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Amjad Khan, Manfredo Atzori, Vincent Andrearczyk and Henning Müller, Generalizing Convolution Neural Networks on Stain Color Heterogeneous Data for Computational Pathology, in: SPIE Medical Imaging, Houston, TX, USA,, 2020

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Lorenz Achim Kuhn, Daniel Abler, Jonas Richiardi, Andreas Hottinger, Luis Schiappacasse, Vincent Dunet, Adrien Depeursinge and Vincent Andrearczyk, AI-based response assessment and prediction in longitudinal imaging for brain metastases treated with stereotactic radiosurgery, in: Learning with Longitudinal Medical Images and Data at MICCAI 2025, 2025

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Jianning Li, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Yuan Jin, Narmada Ambigapathy, Enrico Nasca, Naida Solak, Gian Marco Melito, Afaque R. Memon, Xiaojun Chen, Jan Stefan Kirschke, Ezequiel de la Rosa, Patrich Ferndinand Christ, Hongwei Bran Li, David G. Ellis, Michele R. Aizenberg, Sergios Gatidis, Thomas Kuestner, Nadya Shusharina, Nicholas Heller, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Anjany Sekuboyina, Maximilian Loeffler, Hans Liebl, Reuben Dorent, Tom Vercauteren, Jonathan Shapey, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Achraf Ben-Hamadou, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Federico Bolelli, Costantino Grana, Luca Lumetti, Hamidreza Salehi, Jun Ma, Yao Zhang, Ramtin Gharleghi, Susann Beier, Eduardo A. Garza-Villarreal, Thania Balducci, Diego Angeles-Valdez, Roberto Souza, Leticia Rittner, Richard Frayne, Yuanfeng Ji, Soumick Chatterjee, Andreas Nuernberger, Joao Pedrosa, Carlos Ferreira, Guilherme Aresta, Antonio Cunha, Aurelio Campilho, Yannick Suter, Jose Garcia, Alain Lalande, Emmanuel Audenaert, Claudia Krebs, Timo Van Leeuwen, Evie Vereecke, Rainer Roehrig, Frank Hoelzle, Vahid Badeli, Kathrin Krieger, Matthias Gunzer, Jianxu Chen, Amin Dada, Miriam Balzer, Jana Fragemann, Frederic Jonske, Moritz Rempe, Stanislav Malorodov, Fin H. Bahnsen, Constantin Seibold, Alexander Jaus, Ana Sofia Santos, Mariana Lindo, Andre Ferreira, Victor Alves, Michael Kamp, Amr Abourayya, Felix Nensa, Fabian Hoerst, Alexander Brehmer, Lukas Heine, Lars E. Podleska, Matthias A. Fink, Julius Keyl, Konstantinos Tserpes, Moon-Sung Kim, Shireen Elhabian, Hans Lamecker, Dzenan Zukic, Beatriz Paniagua, Christian Wachinger, Martin Urschler, Luc Duong, Jakob Wasserthal, Peter F. Hoyer, Oliver Basu, Thomas Maal, Max J. H. Witjes, Ping Luo, Bjoern Menze, Mauricio Reyes, Christos Davatzikos, Behrus Puladi, Jens Kleesiek and Jan Egger, MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision, 2023

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Manel Lopez-Melia, Virginie Magnin, Sami Schranz, Vincent Andrearczyk, Adrien Depeursinge, Stéphane Marchand-Maillet and Silke Grabherr, Automatic rib fracture detection on postmortem CT data using deep learning (2025), in: International Journal of Legal Medicine

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Maria Martin Asiain, Mohammadreza Amirian, Oscar Jimenez del Toro, Christoph Aberle, Roger Schaer, Michael Bach, Markus Obmann, Kyriakos Flouris, Henning Müller, Bram Stieltjes, Ender Konukoglu, Vincent Andrearczyk and Adrien Depeursinge, Impact of CT dose on AI performance: A comparison of radiomics, deep, and foundation models in a multi-centric anthropomorphic phantom study (2026), in: Medical Physics

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

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Henning Müller, Vincent Andrearczyk, Oscar Jimenez del Toro, Anjani Dhrangadhariya, Roger Schaer and Manfredo Atzori, Using Publicly Available Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction, in: Multimedia Modeling (MMM 2020), Seoul, Korea, 2020

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Laura O' Mahony, Vincent Andrearczyk, Henning Müller and Mara Graziani, Disentangling Neuron Representations with Concept Vectors, in: Proceedings of the 2nd Explainable AI for Computer Vision (XAI4CV) Workshop at CVPR 2023, 2023

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

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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 and 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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Valentin Oreiller, Julien Fageot, Vincent Andrearczyk, John O. Prior and 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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Sebastian Otalora, Manfredo Atzori, Vincent Andrearczyk, Amjad Khan and Henning Müller, Staining invariant features for improving generalization of deep convolutional neural networks in computational pathology (2019), in: Frontiers in Bioengineering and Biotechnology-Bioinformatics and Computational Biology

[]: (IF 2017= 5.122)

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Sebastian Otalora, Manfredo Atzori, Vincent Andrearczyk and Henning Müller, Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content, in: MICCAI 2018 - Computational Pathology Workshop (COMPAY), Granada, Spain, 2018

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Sebastian Otalora, Oscar Jimenez del Toro, Manfredo Atzori, Amjad Khan, Vincent Andrearczyk and Henning Müller, A systematic comparison of deep learning strategies for weakly supervised Gleason grading,, in: SPIE Medical Imaging, Houstonm, TX, USA, 2020

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Oscar Perdomo, Vincent Andrearczyk, Fabrice Meriaudeau, Henning Müller and Fabio A. Gonzalez, Glaucoma Diagnosis from Eye Fundus Images Based on Deep Morphometric Feature Estimation, in: OMIA at MICCAI, Granada, Spain, 2018

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Numan Saeed, Salma Hassan, Shahad Hardan, Ahmed Aly, Darya Taratynova, Umair Nawaz, Ufaq Khan, Muhammad Ridzuan, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Yutong Xie, Thomas Eugene, Raphä El Metz, M ´ Elanie Dore, Gregory Delpon, Vijay Ram, Kumar Papineni, Kareem Wahid, Cem Dede, Alaa Mohamed, Shawky Ali, Carlos Sjogreen, Mohamed Naser, Clifton D. Fuller, Valentin Oreiller, Mario Jreige, John O. Prior, Catherine Cheze, Le Rest, Olena Tankyevych, Pierre Decazes, Su Ruan, Stephanie Tanadini-Lang, Martin Vallìères, Hesham Elhalawani, Ronan Abgral, Romain Floch, Kevin Kerleguer, Ulrike Schick, Maelle Mauguen, Arman Rahmim and Mohammad Yakub, A Multimodal and Multi-centric Head and Neck Cancer Dataset for Tumor Segmentation and Outcome Prediction (2025)

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Ricky R. Savjani, Michael Lauria, Supratik Bose, Jie Deng, Ye Yuan and Vincent Andrearczyk, Automated Tumor Segmentation in Radiotherapy (2022), in: Seminars in Radiation Oncology, 32:4(319-329)

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Federico Spagnolo, Aarushi Bhardwaj, Pedro M. Gordaliza, Po-Jui Lu, Mario Ocampo-Pineda, Meritxell Bach Cuadra, Xinjie Chen, Batuhan Ayci, Alessandro Cagol, Vincent Andrearczyk, Adrien Depeursinge and Cristina Granziera, Deep-PRL: a deep learning network for the identification of paramagnetic rim lesions in multiple sclerosis, in: ISMRM 2025, 2025

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Federico Spagnolo, Adrien Depeursinge, Vincent Andrearczyk, Pascal Benkert, Stefanie Müller, Lutz Achtnichts, Jochen Vehoff, Giulio Di Santo, Oliver Findling, Andrew Chan, Anke Salmen, Caroline Pot, Claire Bridel, Chiara Zecca, Tobias Johannes Derfuss, Johanna M. Lieb, Luca Remonda, Franca Wagner, Maria-Isabel Vargas Gomez, Renaud Du Pasquier, Patrice Lalive, Emanuele Pravatà, Johannes Weber, Claudio Gobbi, David Leppert, Nataliia Molchanova, Meritxell Bach Cuadra, Po-Jui Lu, Mario Ocampo-Pineda, Lester Melie-Garcia, Cagol Alessandro, Ludwig Kappos, Jens Kuhle and Cristina Granziera, Identification of paramagnetic rim lesions using conventional MRI - a deep learning approach, in: 39th Congress Of The European Committee For Treatment And Research In Multiple Sclerosis (ECTRIMS), 2023

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Federico Spagnolo, Pedro M. Gordaliza, Aarushi Bhardwaj, Po-Jui Lu, Mario Ocampo-Pineda, Meritxell Bach Cuadra, Matthias Weigel, Esther Ruberte, Xinjie Chen, Tommaso Sirito, Alessandro Cagol, Vincent Andrearczyk, Adrien Depeursinge and Cristina Granziera, A multi-modal deep learning network for the classification of paramagnetic rim and remyelinated lesions in multiple sclerosis (2026), in: Multiple Sclerosis Journal

Federico Spagnolo, Pedro M. Gordaliza, Po-Jui Lu, Mario Ocampo-Pineda, Xinjie Chen, Matthias Weigel, Maxence Wynen, Martina Absinta, Pietro Maggi, Meritxell Bach Cuadra, Vincent Andrearczyk, Adrien Depeursinge and Cristina Granziera, Explainability in automatic Paramagnetic Rim Lesion classification, in: 40th Congress Of The European Committee For Treatment And Research In Multiple Sclerosis (ECTRIMS), 2024

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Federico Spagnolo, Nataliia Molchanova, Mario Ocampo-Pineda, Lester Melie-Garcia, Meritxell Bach Cuadra, Cristina Granziera, Vincent Andrearczyk and Adrien Depeursinge, Exploiting XAI maps to improve MS lesion segmentation and detection in MRI, in: Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI, 2025

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Federico Spagnolo, Nataliia Molchanova, Roger Schaer, Meritxell Bach Cuadra, Mario Ocampo-Pineda, Lester Melie-Garcia, Cristina Granziera, Vincent Andrearczyk and Adrien Depeursinge, Instance-level quantitative saliency in multiple sclerosis lesion segmentation (2026), in: Nature Scientific Reports

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Federico Spagnolo, Nataliia Molchanova, Roger Schaer, Mario Ocampo-Pineda, Meritxell Bach Cuadra, Lester Melie-Garcia, Cristina Granziera, Vincent Andrearczyk and Adrien Depeursinge, Instance-level explanations in multiple sclerosis lesion segmentation: a novel localized saliency map, in: ISMRM 2024, 2024

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Federico Spagnolo, Roger Schaer, Vincent Andrearczyk, Nataliia Molchanova, Mara Graziani, Henning Müller, Meritxell Bach Cuadra, Cristina Granziera and Adrien Depeursinge, FLAIR vs MPRAGE contribution to white matter lesion automatic segmentation in MS using localized saliency maps, in: Bern Interpretable AI Symposium (BIAS), 2023

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Eloise Sychold, Clemence de Cournuaud, Nicolas Danthez, Rémy Kinj, Véronique Vallet, Maud Gondre, Andreas Hottinger, Vincent Andrearczyk, Vincent Dunet, Adrien Depeursinge and Luis Schiappacasse, RDTA-08 MULTI-SEQUENTIAL STEREOTACTIC RADIOSURGERY (SRS) FOR BRAIN METASTASES: 10-YEAR EXPERIENCE FROM THE CHUV (LAUSANNE, SWITZERLAND) BRAIN METASTASIS CLINIC (2025), in: Neuro-Oncology Advances, 7:Supplement_2(ii26-ii26)

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Martin Vallìères, Philip Whybra, Vincent Andrearczyk, Roger Schaer, Henning Müller, Adrien Depeursinge and Alex Zwanenburg, Radiomics Analysis Using The Image Biomarker Standardization Initiative (IBSI) Benchmarks And Guidelines, in: Radiological Society of North America (RSNA) 2021 Annual Meeting, 2021

[]: Won a Cum Laude Education Exhibit Award !

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

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Philip Whybra, Alex Zwanenburg, Vincent Andrearczyk, Roger Schaer, Aditya P Apte, Alexandre Ayotte, Bhakti Baheti, Spyridon Bakas, Andrea Bettinelli, Ronald Boellaard, Luca Boldrini, Irène Buvat, Gary J R Cook, Florian Dietsche, Nicola Dinapoli, Hubert S Gabryś, Vicky Goh, Matthias Guckenberger, Mathieu Hatt, Mahdi Hosseinzadeh, Aditi Iyer, Jacopo Lenkowicz, Mahdi A L Loutfi, Steffen Löck, Francesca Marturano, Olivier Morin, Christophe Nioche, Fanny Orlhac, Sarthak Pati, Arman Rahmim, Masoud Seyed, Christopher G Rookyard, Mohammad R Salmanpour, Andreas Schindele, Isaac Shiri, Emiliano Spezi, Stephanie Tanadini-Lang, Florent Tixier, Taman Upadhaya, Vincenzo Valentini, Joost J M van Griethuysen, Fereshteh Yousefirizi, Habib Zaidi, Henning Müller, Martin Vallìères and Adrien Depeursinge, The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights (2024), in: Radiology, 310:2

[]: IF 2022: 19.7

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Marek Wodzinski, Tommaso Banzato, Manfredo Atzori, Vincent Andrearczyk, Yashin Dicente Cid and Henning Müller, Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading, in: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC), IEEE, páginas 1758--1761, 2020

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Marek Wodzinski, Tommaso Banzato, Manfredo Atzori, Vincent Andrearczyk, Yashin Dicente Cid and Henning Müller, Training a deep neural network for small and highly heterogeneous MRID datasets for cancer grading, in: EMBC Conference, IEEE, 2020

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Alex Zwanenburg, Mahmoud A. Abdalah, Hugo Aerts, Vincent Andrearczyk, Aditya Apte, Saeed Ashrafinia, Roelof J. Beukinga, Ronald Boellaard, Marta Bogowicz, Luca Boldrini, Adrien Depeursinge, Marie-Charlotte Desseroit, Nicola Dinapoli, Cuong Viet Dinh, Sebastian Echegaray, Issam El Naqa, Andriy Y. Fedorov, Roberto Gatta, Robert J. Gillies, Matthias Guckenberger, Nils Gählert, Michael Götz, Mathieu Hatt, Fabian Isensee, Philippe Lambin, Stefan Leger, Ralph T. H. Leijenaar, Jacopo Lenkowicz, Fiona Lippert, Are Losnegård, Klaus H. Maier-Hein, Olivier Morin, Henning Müller, Sandy Napel, Arman Rahmim, Arvind U. K. Rao, Nanna M. Sijtsema, Jairo Socarras Fernandez, Emiliano Spezi, Roel J. H. M Steenbakkers, Stephanie Tanadini-Lang, Daniela Thorwarth, Esther G. C. Troost, Taman Upadhaya, Vincenzo Valentini, Martin Vallìères, Uulke A. van der Heide, Lisanne V. van Dijk, Joost van Griethuysen, Floris H. P. van Velden, Philip Whybra, Christian Richter and Steffen Löck, Standardized quantitative radiomics for high-throughput image-based phenotyping (2020), in: Radiology, 295:2(328-338)

[]: IF 2018 = 7.608

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