显微镜即掩码：冷冻电子断层扫描正向模型的特权视图与标签
The microscope is the mask: privileged views and labels from a cryo-ET forward model
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中文总结 AI 辅助
该研究利用冷冻电子断层扫描正向模型生成的模拟数据训练模型CARNIVAL，结合LeJEPA自监督框架与模拟特权信息，在真实断层扫描的分类、检测任务中优于对比目标训练的最先进模型。
中文摘要 AI 辅助
我们探索使用模拟数据训练模型，以对从有限倾斜角采集的图像重建的拥挤冷冻电子断层扫描（cryo-ET）体积中的蛋白质进行注释，该体积受测量算子严重破坏。首先，我们利用正向模型引入的破坏，为集成到LeJEPA自监督训练框架中的不变性目标，生成同一精确场景的特定领域增强配对视图。其次，我们利用模拟管线的额外信息（如模拟体积中蛋白质的位置和身份）来指导模型架构和损失函数，使语义信息定位在生成的密集特征体积的蛋白质位置。生成的模型CARNIVAL在真实断层扫描的分类和检测任务上进行评估，未进行微调，使用包含多种蛋白质类型和两种断层扫描处理类型的基准数据集。结果显示，CARNIVAL在模拟数据上训练时，优于采用对比目标但未使用基于正向模型的配对视图或特权信息的最先进模型。
英文摘要
We explore the use of simulated data for training a model for protein annotation in crowded cryo-electron tomography volumes reconstructed from images collected at limited tilt angles and severely corrupted by the measurement operator. Firstly, we leverage the corruptions imposed by the forward model to generate domain-specific augmented paired views of the exact same scene for an invariance objective integrated into the LeJEPA self-supervised training framework. Secondly, we use additional information from the simulation pipeline such as the positions and identity of proteins in the simulated volumes to inform the architecture of the model and the loss function, so that semantic information is localised at protein positions in the resulting dense feature volume. The resulting model, CARNIVAL, is evaluated without finetuning on classification and detection tasks in real tomograms, using a benchmark dataset containing multiple protein types and two tomogram processing types. We show that CARNIVAL outperforms a state-of-the-art model trained using a contrastive objective on simulated data but without forward model-based paired views or privileged information.
发表机构
- MRC Laboratory of Molecular Biology（MRC分子生物学实验室）
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