# Improved Generation of Synthetic Imaging Data Using Feature-Aligned Diffusion

arXiv: https://arxiv.org/abs/2410.00731 · HTML: https://mlsanity.com/paper/2410.00731/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Understanding Hallucinations in Diffusion Models through Mode Interpolation](https://mlsanity.com/paper/2406.09358/) (2024)
- [Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN](https://mlsanity.com/paper/2401.05159/) (2024)
- [MAM-E: Mammographic synthetic image generation with diffusion models](https://mlsanity.com/paper/2311.09822/) (2023)
- [Aligning Diffusion Models with Noise-Conditioned Perception](https://mlsanity.com/paper/2406.17636/) (2024)

## Built on by (most cited first, 1 of 1)

- [MFGDiffusion: Mask-Guided Smoke Synthesis for Enhanced Forest Fire Detection](https://mlsanity.com/paper/2507.11252/) (2025)

## Abstract (arXiv)

Synthetic data generation is an important application of machine learning in the field of medical imaging. While existing approaches have successfully applied fine-tuned diffusion models for synthesizing medical images, we explore potential improvements to this pipeline through feature-aligned diffusion. Our approach aligns intermediate features of the diffusion model to the output features of an expert, and our preliminary findings show an improvement of 9% in generation accuracy and ~0.12 in SSIM diversity. Our approach is also synergistic with existing methods, and easily integrated into diffusion training pipelines for improvements. We make our code available at urlhttps://github.com/lnairGT/Feature-Aligned-Diffusion.