Mara Graziani
Cited by
Cited by
Regression Concept Vectors for Bidirectional Explanations in Histopathology
M Graziani, V Andrearczyk, H Müller
Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC …, 2018
Megane pro: myo-electricity, visual and gaze tracking data acquisitions to improve hand prosthetics
F Giordaniello, M Cognolato, M Graziani, A Gijsberts, V Gregori, G Saetta, ...
2017 International Conference on Rehabilitation Robotics (ICORR), 1148-1153, 2017
Concept attribution: Explaining CNN decisions to physicians
M Graziani, V Andrearczyk, S Marchand-Maillet, H Müller
Computers in biology and medicine 123, 103865, 2020
Improved interpretability for computer-aided severity assessment of Retinopathy of Prematurity
M Graziani, J Brown, V Andrearczyk, V Yildiz, JP Campbell, D Erdogmus, ...
SPIE Medical Imaging 2019, 2019
Semi-automatic training of an object recognition system in scene camera data using gaze tracking and accelerometers
M Cognolato, M Graziani, F Giordaniello, G Saetta, F Bassetto, P Brugger, ...
International Conference on Computer Vision Systems, 175-184, 2017
Reference exascale architecture
M Bobák, L Hluchy, ASZ Belloum, R Cushing, J Meizner, P Nowakowski, ...
2019 15th International Conference on eScience (eScience), 479-487, 2019
Heterogeneous exascale computing
L Hluchý, M Bobák, H Müller, M Graziani, J Maassen, H Spreeuw, ...
Recent Advances in Intelligent Engineering, 81-110, 2020
Interpreting intentionally flawed models with linear probes
M Graziani, H Muller, V Andrearczyk
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
Visualizing and interpreting feature reuse of pretrained CNNs for histopathology
M Graziani, V Andrearczyk, H Müller
Irish Machine Vision and Image Processing Conference, 2019
Guiding cnns towards relevant concepts by multi-task and adversarial learning
M Graziani, S Otálora, H Muller, V Andrearczyk
arXiv preprint arXiv:2008.01478, 2020
Interpretable CNN pruning for preserving scale-covariant features in medical imaging
M Graziani, T Lompech, H Müller, A Depeursinge, V Andrearczyk
Interpretable and Annotation-Efficient Learning for Medical Image Computing …, 2020
Process Data Infrastructure and Data Services
R Cushing, O Valkering, A Belloum, S Madougou, J Maassen, O Habala, ...
Computing and Informatics 39 (4), 724-756, 2020
Breast Histopathology with High-Performance Computing and Deep Learning
M Graziani, I Eggel, V Andrearczyk
Computing and Informatics 39 (4), 780-807, 2020
On the Scale Invariance in State of the Art CNNs Trained on ImageNet
M Graziani, T Lompech, H Müller, A Depeursinge, V Andrearczyk
Machine Learning and Knowledge Extraction 3 (2), 374-391, 2021
Evaluation and Comparison of CNN Visual Explanations for Histopathology
M Graziani, T Lompech, H Müller, V Andrearczyk
XAI workshop at AAAI21, 2021
Consistency of scale equivariance in internal representations of CNNs
V Andrearczyk, M Graziani, H Müller, A Depeursinge
Irish Machine Vision and Image Processing, 2020
Sharpening Local Interpretable Model-Agnostic Explanations for Histopathology: Improved Understandability and Reliability
M Graziani, I Palatnik de Sousa, MMBR Vellasco, E Costa da Silva, ...
International Conference on Medical Image Computing and Computer-Assisted …, 2021
Learning Interpretable Pathology Features by Multi-task and Adversarial Training Improves CNN Generalization
M Graziani, S Otalora, S Marchand-Maillet, H Müller, V Andrearczyk
Improved Interpretability and Generalisation for Deep Learning
M Graziani
University of Cambridge, MPhil in Machine Learning, Speech and Language …, 2017
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