基于扩散模型的多任务脑肿瘤图像修复：一份方法学报告
Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report
AI总结：
针对医学数据集稀缺影响深度学习性能的问题，提出基于DDPM的多任务脑肿瘤图像修复方法，在多种脑MRI序列上生成高质量且多样的合成数据，并提供了概念验证与开源接口。
摘要：
尽管将深度学习（DL）模型应用于医学影像的兴趣日益增长，但医学数据集的典型稀缺性和不平衡性会严重影响DL模型的性能。生成可以在不损害患者隐私的情况下自由共享的合成数据，是解决这些困难的一种众所周知的技术。图像修复算法是DL生成模型的一个子集，它可以改变输入图像的一个或多个区域，同时匹配其周围上下文，在某些情况下，还能匹配非成像输入条件。尽管医学影像数据的大多数修复技术使用生成对抗网络（GAN），但由于其输出多样性有限，这些算法的性能通常不理想，这是GAN中一个已知的问题。去噪扩散概率模型（DDPM）是最近引入的一类生成网络，可以生成与GAN质量相当但输出多样的结果。在本文中，我们描述了一个DDPM，用于对具有各种序列的脑MRI的2D轴向切片执行多个修复任务，并展示了其在各种评估场景中性能的概念验证示例。我们的模型和用于尝试我们工具的公共在线接口可在以下网址获得：https://github.com/Mayo-Radiology-Informatics-Lab/MBTI
英文摘要：
Despite the ever-increasing interest in applying deep learning (DL) models to medical imaging, the typical scarcity and imbalance of medical datasets can severely impact the performance of DL models. The generation of synthetic data that might be freely shared without compromising patient privacy is a well-known technique for addressing these difficulties. Inpainting algorithms are a subset of DL generative models that can alter one or more regions of an input image while matching its surrounding context and, in certain cases, non-imaging input conditions. Although the majority of inpainting techniques for medical imaging data use generative adversarial networks (GANs), the performance of these algorithms is frequently suboptimal due to their limited output variety, a problem that is already well-known for GANs. Denoising diffusion probabilistic models (DDPMs) are a recently introduced family of generative networks that can generate results of comparable quality to GANs, but with diverse outputs. In this paper, we describe a DDPM to execute multiple inpainting tasks on 2D axial slices of brain MRI with various sequences, and present proof-of-concept examples of its performance in a variety of evaluation scenarios. Our model and a public online interface to try our tool are available at: https://github.com/Mayo-Radiology-Informatics-Lab/MBTI
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