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Mara Graziani
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2020 – today
- 2023
- [j7]Mara Graziani
, Lidia Dutkiewicz, Davide Calvaresi, José Pereira Amorim
A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences. Artif. Intell. Rev. 56(4): 3473-3504 (2023) - [j6]Mara Graziani, Laura O'Mahony, An-Phi Nguyen, Henning Müller, Vincent Andrearczyk:
Uncovering Unique Concept Vectors through Latent Space Decomposition. Trans. Mach. Learn. Res. 2023 (2023) - [c12]Laura O'Mahony, Vincent Andrearczyk, Henning Müller
Disentangling Neuron Representations with Concept Vectors. CVPR Workshops 2023: 3770-3775 - [c11]Nataliia Molchanova, Vatsal Raina, Andrey Malinin, Francesco La Rosa, Henning Müller
Novel Structural-Scale Uncertainty Measures and Error Retention Curves: Application to Multiple Sclerosis. ISBI 2023: 1-5 - [c10]Vatsal Raina, Nataliia Molchanova, Mara Graziani, Andrey Malinin, Henning Müller
Tackling Bias in the Dice Similarity Coefficient: Introducing NDSC for White Matter Lesion Segmentation. ISBI 2023: 1-5 - [i11]Vatsal Raina, Nataliia Molchanova, Mara Graziani, Andrey Malinin, Henning Müller
Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation. CoRR abs/2302.05432 (2023) - [i10]Omar S. M. El Nahhas
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides. CoRR abs/2304.05153 (2023) - [i9]Laura O'Mahony, Vincent Andrearczyk, Henning Müller
Disentangling Neuron Representations with Concept Vectors. CoRR abs/2304.09707 (2023) - [i8]Mara Graziani, Laura O'Mahony, An-Phi Nguyen, Henning Müller, Vincent Andrearczyk:
Uncovering Unique Concept Vectors through Latent Space Decomposition. CoRR abs/2307.06913 (2023) - [i7]Nataliia Molchanova, Vatsal Raina, Andrey Malinin, Francesco La Rosa, Adrien Depeursinge, Mark J. F. Gales, Cristina Granziera, Henning Müller
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation. CoRR abs/2311.08931 (2023) - 2022
- [c9]Mara Graziani, Niccolò Marini, Nicolas Deutschmann, Nikita Janakarajan, Henning Müller
Attention-Based Interpretable Regression of Gene Expression in Histology. iMIMIC@MICCAI 2022: 44-60 - [i6]Andrey Malinin, Andreas Athanasopoulos, Muhamed Barakovic, Meritxell Bach Cuadra, Mark J. F. Gales, Cristina Granziera, Mara Graziani, Nikolay Kartashev, Konstantinos Kyriakopoulos, Po-Jui Lu, Nataliia Molchanova, Antonis Nikitakis, Vatsal Raina, Francesco La Rosa, Eli Sivena, Vasileios Tsarsitalidis, Efi Tsompopoulou, Elena Volf:
Shifts 2.0: Extending The Dataset of Real Distributional Shifts. CoRR abs/2206.15407 (2022) - [i5]Mara Graziani, Niccolò Marini, Nicolas Deutschmann, Nikita Janakarajan, Henning Müller
Attention-based Interpretable Regression of Gene Expression in Histology. CoRR abs/2208.13776 (2022) - [i4]Nataliia Molchanova, Vatsal Raina, Andrey Malinin, Francesco La Rosa, Henning Müller
Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis. CoRR abs/2211.04825 (2022) - 2021
- [b1]Mara Graziani:
Interpretability of Deep Learning for Medical Image Classification: Improved Understandability and Generalization. University of Geneva, Switzerland, 2021 - [j5]Mara Graziani
On the Scale Invariance in State of the Art CNNs Trained on ImageNet. Mach. Learn. Knowl. Extr. 3(2): 374-391 (2021) - [c8]Mara Graziani, Iam Palatnik de Sousa, Marley M. B. R. Vellasco, Eduardo Costa da Silva, Henning Müller
Sharpening Local Interpretable Model-Agnostic Explanations for Histopathology: Improved Understandability and Reliability. MICCAI (3) 2021: 540-549 - 2020
- [j4]Martin Bobák
Reference Exascale Architecture (Extended Version). Comput. Informatics 39(4): 644-677 (2020) - [j3]Reginald Cushing
PROCESS Data Infrastructure and Data Services. Comput. Informatics 39(4): 724-756 (2020) - [j2]Mara Graziani
Breast Histopathology with High-Performance Computing and Deep Learning. Comput. Informatics 39(4): 780-807 (2020) - [j1]Mara Graziani, Vincent Andrearczyk, Stéphane Marchand-Maillet
Concept attribution: Explaining CNN decisions to physicians. Comput. Biol. Medicine 123: 103865 (2020) - [c7]Mara Graziani
Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging. iMIMIC/MIL3ID/LABELS@MICCAI 2020: 23-32 - [i3]Mara Graziani, Sebastian Otálora, Henning Müller, Vincent Andrearczyk:
Guiding CNNs towards Relevant Concepts by Multi-task and Adversarial Learning. CoRR abs/2008.01478 (2020)
2010 – 2019
- 2019
- [c6]Martin Bobák
Reference Exascale Architecture. eScience 2019: 479-487 - [c5]Mara Graziani
Interpreting Intentionally Flawed Models with Linear Probes. ICCV Workshops 2019: 743-747 - [c4]Mara Graziani
Improved interpretability for computer-aided severity assessment of retinopathy of prematurity. Medical Imaging: Computer-Aided Diagnosis 2019: 109501R - [i2]Mara Graziani, Vincent Andrearczyk, Henning Müller:
Regression Concept Vectors for Bidirectional Explanations in Histopathology. CoRR abs/1904.04520 (2019) - 2018
- [c3]Mara Graziani
Regression Concept Vectors for Bidirectional Explanations in Histopathology. MLCN/DLF/iMIMIC@MICCAI 2018: 124-132 - 2017
- [c2]Francesca Giordaniello, Matteo Cognolato, Mara Graziani, Arjan Gijsberts, Valentina Gregori, Gianluca Saetta, Anne-Gabrielle Mittaz Hager, Cesare Tiengo
Megane Pro: Myo-electricity, visual and gaze tracking data acquisitions to improve hand prosthetics. ICORR 2017: 1148-1153 - [c1]Matteo Cognolato, Mara Graziani
Semi-automatic Training of an Object Recognition System in Scene Camera Data Using Gaze Tracking and Accelerometers. ICVS 2017: 175-184 - 2015
- [i1]Mara Graziani:
Studying the control of non invasive prosthetic hands over large time spans. CoRR abs/1511.06004 (2015)
Coauthor Index
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