Imaging is a key tool in medical diagnosis but images and be ‘noisy’, containing multiple sources of information and are sometimes incomplete. The multidisciplinary project team will pioneer novel adaptive sampling techniques to streamline the training process of diffusion models used in image analysis. This innovation will significantly reduce computation resources, rendering diffusion models more practical for real-world applications, including medical imaging diagnosis.

This project was supported with funding from the Cambridge Centre for Data-Driven Discovery and Accelerate Programme for Scientific Discovery, made possible by a Donation from Schmidt Futures.

The Missing U for Efficient Diffusion Models S Calvo–Ordonez, C-W Cheng, J Huang, L Zhang, G Yang, C–B Schonlieb, AI Aviles-Rivero Transactions on Machine Learning Research (TMLR) || Arxiv-Link

Single-Shot Plug-and-Play Methods for Inverse Problems Y Cheng, L Zhang*, Z Shen*, S Wang, L Yu, RH Chan, C-B Schonlieb and AI Aviles-Rivero Preprint Version — Arxiv-Link

Beyond U: Making Diffusion Models Faster & Lighter S Calvo–Ordonez, J Huang, L Zhang, G Yang, C–B Schonlieb, AI Aviles-Rivero NeurIPS 2023 W/ on Diffusion Models Arxiv-Link || Conference || Code

CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI? J Huang,  AI Aviles-Rivero,  C-B Schönlieb and G Yang MICCAI 2023 Arxiv-Link || Conference || Code

DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification. Y Yang, H Fu,  AI Aviles-Rivero,  C-B Schönlieb and L Zhu MICCAI 2023 (early accept) Arxiv-Link || Conference || Code

TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems. K. Wei, AI Aviles-Rivero, J Liang, Y. Fu, H Huang and C-B Schönlieb Journal of Machine Learning Research (JMLR)  Arxiv-Link || Journal Link || Code Repository 🏆Extended Version of our ICML 2020 Outstanding Paper Award