Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarios

Posted on by Alessandra

05/07/2025

Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarios

An oral contribution was presented by Emir Ahmed from the National Physical Laboratory (NPL), UK at the International Joint Conference on Neural Networks (IJCNN), held in Rome (Italy) from the 30th of June to the 5th of July 2025

The talk described the work on evaluation of uncertainty quantification metrics applied to CycleGAN and diffusion-based models for style transfer in mammography between different patient demographics.

Authors: Ciaran Bench1, Emir Ahmed1 and Spencer A. Thomas1

  1. Department of Data Science and AI, National Physical Laboratory (NPL), UK
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The project 22HLT05 MAIBAI, started in September 2023, has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. Funding for the UK partners is provided by Innovate UK under the Horizon Europe Guarantee Extension.

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EURAMET. Neither the European Union nor the granting authority can be held responsible for them.

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