@inproceedings{
li2024pathup,
title={PathUp: Patch-wise Timestep Tracking for Multi-class Large Pathology Image Synthesising Diffusion Model},
author={Jingxiong Li and Sunyi Zheng and Chenglu Zhu and Yuxuan Sun and Pingyi Chen and Zhongyi Shui and Yunlong Zhang and Honglin Li and Lin Yang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=A7VkIoEELI}
}
In digital pathology, cancer lesions are identified by analyzing the spatial context within pathology images. Synthesizing such complex spatial context is challenging as pathology whole slide images typically exhibit high resolution, low inter-class variety, and are sparsely labeled. To address these challenges, we propose PathUp, a novel diffusion model tailored for the synthesis of multi-class high-resolution pathology images. Our approach includes a latent space patch-wise timestep tracking, which helps to generate high-quality images without tiling artifacts. Expert pathology knowledge is integrated into the model through our patho-align mechanism. To ensure robust generation of lesion subtypes and scale information, we introduce a feature entropy loss function. We substantiate the effectiveness of our method through both qualitative and quantitative evaluations, supplemented by assessments from human experts, demonstrating the authenticity of the synthetic data produced. Furthermore, we highlight the potential utility of our generated images as an augmentation method, thereby enhancing the performance of downstream tasks such as cancer subtype classification.
PathUp: Patch-wise Timestep Tracking for Multi-class Large Pathology Image Synthesising Diffusion Model
Jingxiong Li Sunyi Zheng Chenglu Zhu Yuxuan Sun Pingyi Chen Zhongyi Shui Yunlong Zhang Honglin Li Lin Yang