| Regression Concept Vectors for Bidirectional Explanations in Histopathology M Graziani, V Andrearczyk, H Müller Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC …, 2018 | 72* | 2018 |
| Concept attribution: Explaining CNN decisions to physicians M Graziani, V Andrearczyk, S Marchand-Maillet, H Müller Computers in biology and medicine 123, 103865, 2020 | 27 | 2020 |
| 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 | 18 | 2017 |
| 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 | 16 | 2019 |
| Heterogeneous exascale computing L Hluchı, M Bobák, H Müller, M Graziani, J Maassen, H Spreeuw, ... Recent Advances in Intelligent Engineering, 81-110, 2020 | 13* | 2020 |
| Visualizing and interpreting feature reuse of pretrained CNNs for histopathology M Graziani, V Andrearczyk, H Müller Irish Machine Vision and Image Processing Conference, 2019 | 11 | 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 | 9 | 2017 |
| Interpreting intentionally flawed models with linear probes M Graziani, H Muller, V Andrearczyk Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 8 | 2019 |
| Learning Interpretable Diagnostic Features of Tumor by Multi-task Adversarial Training of Convolutional Networks: Improved Generalization M Graziani, S Otalora, S Marchand-Maillet, H Müller, V Andrearczyk | 6* | 2022 |
| 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 | 6 | 2020 |
| Breast histopathology with high-performance computing and deep learning M Graziani, I Eggel, V Andrearczyk Computing and Informatics 39 (4), 780-807, 2020 | 5 | 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 | 4 | 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 | 3 | 2021 |
| Evaluation and Comparison of CNN Visual Explanations for Histopathology M Graziani, T Lompech, H Müller, V Andrearczyk XAI workshop at AAAI21, 2021 | 2 | 2021 |
| 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 | 1 | 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 | 1 | 2020 |
| Interpretability of Deep Learning for Medical Image Classification: Improved Understandability and Generalization M Graziani University of Geneva, 2021 | | 2021 |
| Learning Interpretable Diagnostic Features of Tumor by Multi-task Adversarial Training of Convolutional Networks: Improved Generalization M Graziani, S Otalora, S Marchand-Maillet, H Müller, V Andrearczyk | | 2020 |
| Improved Interpretability and Generalisation for Deep Learning M Graziani University of Cambridge, MPhil in Machine Learning, Speech and Language …, 2017 | | 2017 |
Henning MüllerProfessor in computer science at HES-SO & in radiology at the University of GenevaVerified email at hevs.ch
