# Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathology

arXiv: https://arxiv.org/abs/2312.01152 · HTML: https://mlsanity.com/paper/2312.01152/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Placed in

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

## Builds on (most cited first, 6 of 6)

- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Any-resolution Training for High-resolution Image Synthesis](https://mlsanity.com/paper/2204.07156/) (2022)
- [Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis](https://mlsanity.com/paper/2303.12644/) (2023)
- [You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery](https://mlsanity.com/paper/1805.09512/) (2018)
- [Federated Stain Normalization for Computational Pathology](https://mlsanity.com/paper/2209.14849/) (2022)
- [Realistic Data Enrichment for Robust Image Segmentation in Histopathology](https://mlsanity.com/paper/2304.09534/) (2023)

## Abstract (arXiv)

Diagnoses from histopathology images rely on information from both high and low resolutions of Whole Slide Images. Ultra-Resolution Cascaded Diffusion Models (URCDMs) allow for the synthesis of high-resolution images that are realistic at all magnification levels, focusing not only on fidelity but also on long-distance spatial coherency. Our model beats existing methods, improving the pFID-50k [2] score by 110.63 to 39.52 pFID-50k. Additionally, a human expert evaluation study was performed, reaching a weighted Mean Absolute Error (MAE) of 0.11 for the Lower Resolution Diffusion Models and a weighted MAE of 0.22 for the URCDM.