Pedro Sanchez — accepted AI-conference papers

2024

Benchmarking Counterfactual Image Generation

Thomas Melistas, Nikos Spyrou, Nefeli Gkouti, Pedro Sanchez, Athanasios Vlontzos, Yannis Panagakis +2

NeurIPS 2024poster

Generative AI has revolutionised visual content editing, empowering users to effortlessly modify images and videos. However, not all edits are equal. To perform realistic edits in domains such as natural image or medical imaging, modifications must respect causal relationships inherent to the data g…

Cited by 6 Source ↗PDF ↗code ↗Scholar ↗

2024

RadEdit: stress-testing biomedical vision models via diffusion image editing

Fernando Pérez-García, Sam Bond-Taylor, Pedro Sanchez, Boris van Breugel, Daniel Coelho de Castro, Harshita Sharma +8

ECCV 2024poster

"Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. This work proposes using generative image editing to simulate dataset shifts and diagnose failure modes of biomedical visi…

Cited by 9 Source ↗PDF ↗Scholar ↗

2023

Diffusion Models for Causal Discovery via Topological Ordering

Pedro Sanchez, Xiao Liu, Alison Q O'Neil, Sotirios A. Tsaftaris

ICLR 2023poster

Discovering causal relations from observational data becomes possible with additional assumptions such as considering the functional relations to be constrained as nonlinear with additive noise (ANM). Even with strong assumptions, causal discovery involves an expensive search problem over the space…

Cited by 54 Source ↗PDF ↗code ↗Scholar ↗