I am glad to be presenting at the International DACH Joint symposium on digital pathology and AI in February 2022. Stay tuned for more info!
- November 2021: The course “Introduction to AI Interpretability” of the AIDA program (AI4Media) goes for another semester. Registration to watch-anytime videos and light supervision at introinterpretableai.wordpress.com
- October 2021: “Sharpening LIME for Histopathology: Improved Understandability and Reliability” at MICCAI2021 and at the Visual Intelligence Initiative of the Norwegian center for research-based innovation.
- June 2021: “Interpretability of Deep Learning for Medical Imaging: Improved Understandability and Generalization” at IBM Research Zurich
- May 2021: “Crash course: Introduction to AI Interpretability”. Part of the AIDA program within AI4Media. Get accesso to watch-anytime videos and light supervision at introinterpretableai.wordpress.com
- April 2021: “Introduction to AI Interpretability” at the Applied Machine Learning Days 2021 – Workshop on Building Interpretability for Digital Pathology. Check the slides, the log of the chat with questions and answers and the GitHub repo of the experiments.
- March 2021: Better Model Interpretability for Digital Pathology: “Adapting” rather than “Applying” for the Swiss Digital Pathology Consortium
- February 2021: Presentation at the XAI Workshop AAAI21 “Evaluation and Comparison of CNN Visual Explanations for Histopathology“
- January 2021: A myth-busting attempt for DL interpretability: discussing taxonomies, methodologies and applications to medical imaging. at the CIBM Centre d’Imagerie Biomedicale de Lausanne.
- Invited talk at the SANO Computational Medicine Center on the Emerging Needs of AI for Digital Pathology.
- October 2020: Workshop on Interpretability of Machine Intelligence in Medical Imaging Computing at MICCAI 2020: Interpretable Network Pruning
- Human-centric interpretability of deep learning for digital pathology for the Swiss Digital Pathology Consortium
- Machine Learning Interpretability Inside Out e-talk at IBM Zurich on how to interpret AI models
- PhD First year exam: an extract of my slides
- Improving the interpretability of Retinopathy of Prematurity: a summary on this Medium post of my research published by SPIE Medical Imaging in Computer Assisted Diagnosis
- Swiss Machine Learning Days 2018 Regression Concept Vectors
- August 2018: Seminar at the Argonne National Laboratory
- June 2018: Visit as alumni to the Cambridge Engineering Dept
- Workshop on Interpretability of Machine Intelligence in Medical Imaging Computing at MICCAI 2018