Publications

How do language models learn facts? Dynamics, curricula and hallucinations

Nicolas Zucchet

Jörg Bornschein

Stephanie Chan

Andrew Lampinen

Razvan Pascanu

Soham De

2025-03-27

ArXiv (prépublication)

arxiv.org

PRISM: High-Resolution&Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion

Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, da… (voir plus)ta imbalances, and limited text annotations in datasets. Addressing these challenges requires architectures robust to the unique complexities posed by medical imaging data. The rapid advancements in vision-language foundation models within the natural image domain prompt the question of how they can be adapted for medical imaging tasks. In this work, we present PRISM, a framework that leverages foundation models to generate high-resolution, language-guided medical image counterfactuals using Stable Diffusion. Our approach demonstrates unprecedented precision in selectively modifying spurious correlations (the medical devices) and disease features, enabling the removal and addition of specific attributes while preserving other image characteristics. Through extensive evaluation, we show how PRISM advances counterfactual generation and enables the development of more robust downstream classifiers for clinically deployable solutions. To facilitate broader adoption and research, we make our code publicly available at https://github.com/Amarkr1/PRISM.

2025-03-27

MIDL.io/2025/Conference (présentation orale)

doi.org

openreview.net

StarFlow: Generating Structured Workflow Outputs From Sketch Images

Patrice Béchard

Chao Wang

Amirhossein Abaskohi

Juan A. Rodriguez

Chris Pal

David Vázquez

Spandana Gella