  Data generated in clinical practice often exhibits biases, such as long-tail
imbalance and algorithmic unfairness. This study aims to mitigate these
challenges through data synthesis. Previous efforts in medical imaging
synthesis have struggled with separating lesion information from background
context, leading to difficulties in generating high-quality backgrounds and
limited control over the synthetic output. Inspired by diffusion-based image
inpainting, we propose LeFusion, lesion-focused diffusion models. By
redesigning the diffusion learning objectives to concentrate on lesion areas,
it simplifies the model learning process and enhance the controllability of the
synthetic output, while preserving background by integrating forward-diffused
background contexts into the reverse diffusion process. Furthermore, we
generalize it to jointly handle multi-class lesions, and further introduce a
generative model for lesion masks to increase synthesis diversity. Validated on
the DE-MRI cardiac lesion segmentation dataset (Emidec), our methodology
employs the popular nnUNet to demonstrate that the synthetic data make it
possible to effectively enhance a state-of-the-art model. Code and model are
available at https://github.com/M3DV/LeFusion.
