高效扩散模型助力医学影像生成，提速降耗更精准
Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
- 按去噪、潜空间、小波三类梳理主流扩散模型结构
- 潜空间与小波模型显著降低计算开销，提升推理速度
- 适合医疗图像生成、低资源部署及临床辅助诊断研究者
扩散模型近年在计算机视觉领域崭露头角，在生成式人工智能中表现卓越，能生成高质量合成图像，并已成功应用于多个场景。然而，其训练与生成过程仍面临高计算成本的挑战。本研究聚焦基于扩散模型的生成模型在效率与推理时间上的优化，重点探讨其在自然图像与医学影像中的应用。我们系统归纳了三类核心模型：去噪扩散概率模型（DDPM）、潜空间扩散模型（LDM）和小波扩散模型（WDM），分析它们在自然与医学影像中缓解计算复杂度差距的机制。同时讨论当前局限性及未来在医学影像领域的研究机遇与方向。
原文摘要 · Abstract (English)
The diffusion model has recently emerged as a potent approach in computer vision, demonstrating remarkable performances in the field of generative artificial intelligence. Capable of producing high-quality synthetic images, diffusion models have been successfully applied across a range of applications. However, a significant challenge remains with the high computational cost associated with training and generating these models. This study focuses on the efficiency and inference time of diffusion-based generative models, highlighting their applications in both natural and medical imaging. We present the most recent advances in diffusion models by categorizing them into three key models: the Denoising Diffusion Probabilistic Model (DDPM), the Latent Diffusion Model (LDM), and the Wavelet Diffusion Model (WDM). These models play a crucial role in medical imaging, where producing fast, reliable, and high-quality medical images is essential for accurate analysis of abnormalities and disease diagnosis. We first investigate the general framework of DDPM, LDM, and WDM and discuss the computational complexity gap filled by these models in natural and medical imaging. We then discuss the current limitations of these models as well as the opportunities and future research directions in medical imaging.
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