用深度学习压缩冷冻电镜生物数据，提升存储效率。
Application of Deep Learning in Biological Data Compression
- 用神经网络编码密度信息，替代原始数据存储。
- 压缩比高，重建误差低，支持跨文件通用。
- 适合需要高效存储的科研与教学场景。
冷冻电镜（Cryo-EM）已成为获取高分辨率生物结构的重要工具。尽管其在可视化方面优势明显，但原始数据文件体积庞大，给研究人员和教育工作者带来显著存储挑战。本文研究了深度学习方法，特别是隐式神经表示（INR）在压缩Cryo-EM生物数据中的应用。首先，根据密度阈值提取每个文件的二值化图谱，该密度图具有高度重复性，可被GZIP高效压缩。随后，神经网络训练以编码空间密度信息，仅需存储网络参数和可学习的潜在向量。为提升重建精度，进一步引入位置编码增强空间表征，并采用加权均方误差（MSE）损失函数以平衡不同密度分布的差异。该方法旨在提供一种实用、高效的生物数据压缩方案，适用于科研与教学场景，同时保持合理的压缩率与重建质量。
原文摘要 · Abstract (English)
Cryogenic electron microscopy (Cryo-EM) has become an essential tool for capturing high-resolution biological structures. Despite its advantage in visualizations, the large storage size of Cryo-EM data file poses significant challenges for researchers and educators. This paper investigates the application of deep learning, specifically implicit neural representation (INR), to compress Cryo-EM biological data. The proposed approach first extracts the binary map of each file according to the density threshold. The density map is highly repetitive, ehich can be effectively compressed by GZIP. The neural network then trains to encode spatial density information, allowing the storage of network parameters and learnable latent vectors. To improve reconstruction accuracy, I further incorporate the positional encoding to enhance spatial representation and a weighted Mean Squared Error (MSE) loss function to balance density distribution variations. Using this approach, my aim is to provide a practical and efficient biological data compression solution that can be used for educational and research purpose, while maintaining a reasonable compression ratio and reconstruction quality from file to file.
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