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Program

Session 1: Deep learning (Jan. 8th, 14h-17h30)

Vision Transformers and Language models (abstract)

Matthieu Cord

Sorbonne University, Institut des systèmes intelligents et robotique (ISIR), Paris, France

Deep learning for Biology (abstract)

Christophe Zimmer

Pasteur Institute, Department of Computational Biology, Paris, France

Live Single-cell Imaging of Genome Organization and Dynamics (abstract)

Haitham Shaban

Faculty of Medicine-University of Geneva, and Agora Cancer Research Center, Lausanne, Switzerland.

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Session 2: Deep learning for cell and Single Molecule Tracking (Jan. 9th, 9h30-12h30)

Next-Gen Single Molecule Microscopy

Enhancing Single Molecule Microscopy by integrating Machine Learning and Simulation Tools (abstract)

Ulrike Endesfelder

Bonn University, Institute for Microbiology and Biotechnology, Bonn, Germany

Improving resolution, synthesising data and producing verifiable results (abstract)

Susan Cox

King’s College, Randall Centre for Cell & Molecular Biophysics, Faculty of Life Sciences & Medicine, London, UK

Integrating temporal context for deep-learning based cell segmentation and tracking (abstract)

Maxime Deforet

Sorbonne University, IBPS, Laboratory Jean Perrin, Paris, France

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Session 3: AI for biomedical images and diagnostics (Jan. 9th, 14h-18h)

AI-based Chromatin Imaging Biomarkers (abstract)

G.V. Shivashankar  

ETH Zurich, Paul Scherrer Institute, Switzerland

Reproducibility in machine learning for biomedical imaging (abstract)

Olivier Colliot

Sorbonne University, Paris Brain Institute, CNRS, Inria, Inserm, AP-HP

Prediction of molecular features from Whole Slide Images (abstract)

Thomas Walter

Centre for Computational Biology, Ecole des Mines de Paris, Institute Curie, Paris, France

Hybrid AI for building 3D individual patient's models

from medical images for mini-invasive pediatric surgery (abstract)

Isabelle Bloch

Sorbonne University, CNRS, LIP6, Paris, France