基于VQ-VAE的神经形态学保留体积数据编码
Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE
摘要：
深度学习架构的效率和紧凑性不断提高，加上硬件的改进，使得医学体积数据的复杂和高维建模能够以更高的分辨率进行。最近，矢量量化变分自编码器（VQ-VAE）已被提出作为一种有效的生成式无监督学习方法，可以将图像编码到其初始大小的一小部分，同时保持其解码保真度。在这里，我们展示了一个VQ-VAE启发的网络可以有效地编码一个全分辨率的3D脑体积，压缩数据到原始大小的$0.825\%$，同时保持图像保真度，并显着优于以前的国家的最先进的。然后，我们证明了VQ-VAE解码图像保留了原始数据的形态特征，通过基于体素的形态和分割实验。最后，我们证明了这样的模型可以预先训练，然后在不同的数据集上进行微调，而不会引入偏差。
英文摘要：
The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical volumetric data at higher resolutions. Recently, Vector-Quantised Variational Autoencoders (VQ-VAE) have been proposed as an efficient generative unsupervised learning approach that can encode images to a small percentage of their initial size, while preserving their decoded fidelity. Here, we show a VQ-VAE inspired network can efficiently encode a full-resolution 3D brain volume, compressing the data to $0.825\%$ of the original size while maintaining image fidelity, and significantly outperforming the previous state-of-the-art. We then demonstrate that VQ-VAE decoded images preserve the morphological characteristics of the original data through voxel-based morphology and segmentation experiments. Lastly, we show that such models can be pre-trained and then fine-tuned on different datasets without the introduction of bias.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
