论文标题
跨模式神经图合成：一项调查
Cross-Modality Neuroimage Synthesis: A Survey
论文作者
论文摘要
多模式成像改善了疾病的诊断，并揭示了具有解剖学特性的组织中的不同偏差。完全排列和配对的多模式神经影像数据的存在证明了其在大脑研究中的有效性。但是，收集完全对齐和配对的数据是昂贵甚至不切实际的，因为它面临许多困难，包括高成本，长期获取时间，图像腐败和隐私问题。另一种解决方案是探索无监督或弱监督的学习方法，以综合缺乏神经成像数据。在本文中，我们从基于综合的弱监督和无监督的设置，损失函数，评估指标，成像模式，数据集和下游应用程序的角度，对神经图像的跨模式合成进行了全面综述。我们首先要强调跨模式神经图的几个开放挑战。然后，我们讨论不同监督下的跨模式合成方法的代表性体系结构。接下来是逐步深入分析，以评估跨模式神经图的合成如何改善其下游任务的性能。最后，我们总结了现有的研究结果，并指出了未来的研究方向。所有资源均可在https://github.com/m-3lab/awesome-multimodal-brain-imimage-systhesis中获得
Multi-modality imaging improves disease diagnosis and reveals distinct deviations in tissues with anatomical properties. The existence of completely aligned and paired multi-modality neuroimaging data has proved its effectiveness in brain research. However, collecting fully aligned and paired data is expensive or even impractical, since it faces many difficulties, including high cost, long acquisition time, image corruption, and privacy issues. An alternative solution is to explore unsupervised or weakly supervised learning methods to synthesize the absent neuroimaging data. In this paper, we provide a comprehensive review of cross-modality synthesis for neuroimages, from the perspectives of weakly supervised and unsupervised settings, loss functions, evaluation metrics, imaging modalities, datasets, and downstream applications based on synthesis. We begin by highlighting several opening challenges for cross-modality neuroimage synthesis. Then, we discuss representative architectures of cross-modality synthesis methods under different supervisions. This is followed by a stepwise in-depth analysis to evaluate how cross-modality neuroimage synthesis improves the performance of its downstream tasks. Finally, we summarize the existing research findings and point out future research directions. All resources are available at https://github.com/M-3LAB/awesome-multimodal-brain-image-systhesis
