Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing

Autor:	Bieder, Florentin, Wolleb, Julia, Durrer, Alicia, Sandkühler, Robin, Cattin, Philippe C.
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Zdroj:	Medical Imaging with Deep Learning, PMLR 227:552-567, 2024
Druh dokumentu:	Working Paper
Popis:	Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks. They do, however, require a large amount of computational resources. This limits their application to medical tasks, where we often deal with large 3D volumes, like high-resolution three-dimensional data. In this work, we present a number of different ways to reduce the resource consumption for 3D diffusion models and apply them to a dataset of 3D images. The main contribution of this paper is the memory-efficient patch-based diffusion model \textit{PatchDDM}, which can be applied to the total volume during inference while the training is performed only on patches. While the proposed diffusion model can be applied to any image generation tasks, we evaluate the method on the tumor segmentation task of the BraTS2020 dataset and demonstrate that we can generate meaningful three-dimensional segmentations. Comment: Accepted at MIDL 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.15288 Zobrazit plný text záznamu View this record from Arxiv