Mara Graziani - HES SO Valais Publications - Aigaion 2.0

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First name(s):	Mara
Last name(s):	Graziani

Mara Graziani, Lidia Dutkiewicz, Davide Calvaresi, Jose Pereira Amorim, Katerina Yordanova, Mor Vered, Rahul Nair, Pedro H. Abreu, Tobias Blanke, Valeria Pulignano, John O. Prior, Lode Lauwaert, Wessel Reijers, Adrien Depeursinge, Vincent Andrearczyk and Henning Müller, A Global Taxonomy of Interpretable AI: Unifying the Terminology for the Technical and Social Sciences (2022), in: Artificial Intelligence Review, 56(3473–3504)

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Mara Graziani, Ivan Eggel, Francois Deligand, Martin Bobák, Vincent Andrearczyk and Henning Müller, Breast Histopathology with High-Performance Computing and Deep Learning (2020), in: Computer and Informatics

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Martin Bobák, Ladislav Hluchý, Ondrej Habala, Viet Tran, Reginald Cushing, Onno Valkering, Adam S. Z. Belloum, Mara Graziani, Henning Müller, Souley Madougou and Jason Maassen, Reference Exascale Architecture - Extended Version (2020), in: Computer And Informatics

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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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[DOI]

Reginald Cushing, Onno Valkering, Adam S. Z. Belloum, Souley Madougou, Martin Bobák, Ondrej Habala, Viet Tran, Jan Meizner, Piotr Nowakowski, Mara Graziani and Henning Müller, PROCESS Data Infrastructure and Data Service (2020), in: Computing and Informatics

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Mara Graziani, Vincent Andrearczyk and Henning Müller, Regression Concept Vectors for Bidirectional Explanations in Histopathology (2018), in: Lecture Notes in Computer Science, Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2018(8)

[]: Best paper award

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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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[DOI]

Nataliia Molchanova, Raina Vatsal, Andrey Malinin, Francesco La Rosa, Henning Müller, Mark Gales, Cristina Granziera, Mara Graziani and Meritxell Bach Cuadra, NOVEL STRUCTURAL-SCALE UNCERTAINTY MEASURES AND ERROR RETENTION CURVES: APPLICATION TO MULTIPLE SCLEROSIS (2023), in: Proceedings of International Symposium of Biomedical Imaging 2023

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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, Vincent Andrearczyk and Henning Müller, Deep learning interpretability: measuring the relevance of clinical concepts in convolutional neural networks features, Elsevier, volume 157-192, 2023

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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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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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Raina Vatsal, Nataliia Molchanova, Mara Graziani, Andrey Malinin, Henning Müller, Meritxell Bach Cuadra and Mark Gales, Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation, in: IEEE International Symposium on Biomedical Imaging, 2023

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Nataliia Molchanova, Raina Vatsal, Francesco La Rosa, Andrey Malinin, Henning Müller, Mark Gales, Cristina Granziera, Mara Graziani and Meritxell Bach Cuadra, Towards Informative Uncertainty Measures for MRI Segmentation in Clinical Practice: Application to Multiple Sclerosis, in: Bern Interpretable Symposium (BIAS) 2023, 2023

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Mara Graziani, Niccolò Marini, Nicolas Deutschmann, Nikita Janakarajan, Henning Müller and Maria Rodriguez Martinez, Attention-based Interpretable Regression of Gene Expression in Histology, in: Proceedings of the The Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC) at MICCAI 2022, 2022

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Mara Graziani, Vincent Andrearczyk and Henning Müller, Deep Learning Interpretability: Measureing the relevance of clinical concepts in CNN features, in: State of the art in neural networks, Elsevier, 2022

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

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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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Ladislav Hluchy, Martin Bobák, Henning Müller, Mara Graziani, Jason Maassen, Hanno Spreeuw, Matti Heikkurinen, Jörg Pancake-Steeg, Stefan Spahr, Nils Otto vor dem Gentschen Felde, Maximilian Höb, Jan Schmidt, Adam S. Z. Belloum, Reginald Cushing, Piotr Nowakowski, Jan Meizner, Katarzyna Rycerz, Bartosz Wilk, Marian Bubak, Ondrej Habala, Martin Seleng, Stefan Dlugolinsky, Viet Tran and Giang Nguyen, Heterogeneous exascale computing, in: INES 2018 conference, Springer, 2019

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Mara Graziani, James M. Brown, Vincent Andrearczyk, Veysi Yildiz, J. Peter Campbelle, Deniz Erdogmus, Stratis Ioannidis, Michael F. Chiange, Jayashree Kalpathy-Cramer and Henning Müller, Improved interpretability for computer-aided severity assessment of retinopathy of prematurity, in: SPIE Medical Imaging, San Diego, CA, USA, 2019

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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, Vincent Andrearczyk and Henning Müller, Visualizing and interpreting feature reuse of pretrained CNNs for histopathology, in: IMVIP 2019, Dublin, Ireland, 2019

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Manfredo Atzori, Matteo Cognolato, Diego Faccio, Gianluca Saetta, Arjan Gijsberts, Valentina Gregori, Mara Graziani, Francesca Giordaniello, Andrea Gigli, Francesca Palermo, Peter Brugger, Barbara Caputo, Henning Müller and Franco Bassetto, Myo-electricity and gaze tracking data to improve hand prosthetics and neuro-cognitive examination, in: FESSH 2018, Copenhagen, Denmark, 2018

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Francesca Giordaniello, Matteo Cognolato, Mara Graziani, Arjan Gijsberts, Valentina Gregori, Gianluca Saetta, Anne-Gabrielle Mittaz Hager, Cesare Tiengo, Franco Bassetto, Peter Brugger, Barbara Caputo, Henning Müller and Manfredo Atzori, Megane Pro: myo-electricity, visual and gaze tracking integration as a resource for dexterous hand prosthetics, in: IEEE International Conference on Rehabilitation Robotics, London, UK, 2017

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Matteo Cognolato, Mara Graziani, Francesca Giordaniello, Gianluca Saetta, Franco Bassetto, Peter Brugger, Barbara Caputo, Henning Müller and Manfredo Atzori, Semi-automatic training of an object recognition system in scene camera data using gaze tracking and accelerometers, in: International Conference on Computer Vision Systems (ICVS), Shenzhen (China), 2017

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Martin Bobák, Ladislav Hluchý, Mara Graziani and Henning Müller, Machine Learning in Medical Imaging, Radenci, Slovenia, 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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Andrey Malinin, Andreas Athanasopoulos, Muhamed Barakovic, Meritxell Bach Cuadra, Mark Gales, Cristina Granziera, Mara Graziani, Nikolay Kartashev, Konstantinos Kyriakopoulos, Po-Jui Lu, Nataliia Molchanova, Antonis Nikitakis, Raina Vatsal, Francesco La Rosa, Eli Sivena, Vasileios Tsarsitalidis, Efi Tsompopoulou and Elena Volf, Shifts 2.0: Extending The Dataset of Real Distributional Shifts, arXiv, 2022

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[DOI]
[URL]

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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Nataliia Molchanova, Raina Vatsal, Francesco La Rosa, Andrey Malinin, Henning Müller, Mark Gales, Cristina Granziera, Mara Graziani and Meritxell Bach Cuadra, Towards Informative Uncertainty Measures for MRI Segmentation in Clinical Practice: Application to Multiple Sclerosis, ISMRM & ISMRT 2023 Annual Meeting & Exhibition, 2023

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Raina Vatsal, Nataliia Molchanova, Mara Graziani, Andrey Malinin, Po-Jui Lu, Weigel Matthias, Henning Müller, Cristina Granziera, Meritxell Bach Cuadra and Mark Gales, The Normalised Dice Similarity Coefficient for MS: tackling lesion load biases in white matter and cortical lesion segmentation, European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) Conference 2023, 2023

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Nataliia Molchanova, Cagol Alessandro, Mario Ocampo-Pineda, Chen Xinjie, Weigel Matthias, Henning Müller, Mara Graziani, Cristina Granziera and Meritxell Bach Cuadra, Deep learning uncertainty quantification of cortical lesions in MP2RAGE for missed lesions discovery, European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) Conference 2023, 2023

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Omar SM El Nahhas, Chiara ML Loeffler, Zunamys I Carrero, Marko van Treeck, Fiona R Kolbinger, Katherine J Hewitt, Hanna S Muti, Mara Graziani, Qinghe Zeng, Julien Calderaro, Nadina Ortiz-Brüchle, Tanwei Yuan, Michael Hoffmeister, Hermann Brenner, Alexander Brobeil, Jorge S Reis-Filho and Jakob Nikolas Kather, Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

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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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