CryoHype：用基于 Transformer 的超网络重建上千个冷冻电镜结构
CryoHype: Reconstructing a thousand cryo-EM structures with transformer-based hypernetworks
把超网络与隐式神经表示结合，用输入结构索引动态生成 INR 权重，为视觉中「一次前向重建大量不同实例」的摊销式重建提供了可复用范式；固定位姿下从混合粒子图像中分离上千个结构，也可迁移到多目标、多实例的逆渲染与神经场重建。
原文依据 a transformer-based hypernetwork for cryo-EM reconstruction that dynamically adjusts the weights of an implicit neural representation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论冷冻电子显微镜（cryo-EM）是测定动态生物分子复合物三维结构不可或缺的技术。虽然它通常用于对单一分子种类成像，但 cryo-EM 在以一种高通量方式同时测定多个目标的结构方面具有巨大潜力。然而，现有方法通常侧重于对单个或极少数结构内的构象异质性建模，并非为解析由众多不同分子种类混合而成的组分异质性而设计。为应对这一挑战，我们提出 CryoHype，一种基于 Transformer 的超网络，用于冷冻电镜重建，它根据输入结构动态调整隐式神经表示的权重。借助 CryoHype，我们在固定位姿设定下成功从冷冻电镜成像中重建出 1000 个不同的结构，且无需任何关于所存在结构的先验知识，这超出了任何现有算法的能力。
英文原文摘要 1004 字符
Cryo-electron microscopy (cryo-EM) is an indispensable technique for determining the 3D structures of dynamic biomolecular complexes. While typically applied to image a single molecular species, cryo-EM holds great potential for structure determination of many targets simultaneously in a high-throughput fashion. However, existing methods typically focus on modeling conformational heterogeneity within a single or very few structures and are not designed to resolve compositional heterogeneity arising from mixtures of many distinct molecular species. To address this challenge, we propose CryoHype, a transformer-based hypernetwork for cryo-EM reconstruction that dynamically adjusts the weights of an implicit neural representation based on the input structure. Using CryoHype, we successfully reconstruct 1,000 distinct structures from cryo-EM imaging in the fixed-pose setting without any pre-existing knowledge of the structures present, which is beyond the capabilities of any existing algorithm.