Motivation: Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool that visualizes the structural and spatial organization of macromolecules at a near-native state in single cells, which has broad applications in life science. However, the systematic structural recognition and recovery of macromolecules captured by cryo-ET are difficult due to high structural complexity and imaging limits. Deep learning based subtomogram classification have played critical roles for such tasks. As supervised approaches, however, their performance relies on sufficient and laborious annotation on a large training dataset. Results: To alleviate this major labeling burden, we proposed a Hybrid Active Learning (HAL) framework for querying subtomograms for labelling from a large unlabeled subtomogram pool. Firstly, HAL adopts uncertainty sampling to select the subtomograms that have the most uncertain predictions. Moreover, to mitigate the sampling bias caused by such strategy, a discriminator is introduced to judge if a certain subtomogram is labeled or unlabeled and subsequently the model queries the subtomogram that have higher probabilities to be unlabeled. Additionally, HAL introduces a subset sampling strategy to improve the diversity of the query set, so that the information overlap is decreased between the queried batches and the algorithmic efficiency is improved. Our experiments on subtomogram classification tasks using both simulated and real data demonstrate that we can achieve comparable testing performance (on average only 3% accuracy drop) by using less than 30% of the labeled subtomograms, which shows a very promising result for subtomogram classification task with limited labeling resources.
翻译:动力: Cryo- Eplectron Tomagraphy( cryo- ET) 是一个3D生物成像工具,可以将大型培训数据集中的大型分子结构与空间组织化为3D生物成像工具,在生命科学中具有广泛的应用。然而,由于高结构复杂性和成像限制,由Cryo- Eplectron Tomagraphy(Cryo- ET) 捕获的大型分子成像仪(Cryono- Eectronectronic Tomgraphy (creat-Enter-ET) 是一个3D生物成像仪的3Diographic 工具。结果:为了减轻这个主要标签负担,我们建议一个混合学习(HAL)框架,用来从一个没有标签的大型子图谱库中查询子图的标签。首先,HAL采用不确定性抽样来选择具有最不确定预测力的子图。此外,为了减轻这种策略导致的取样偏差,我们引入了一个子图或未贴标签的下,然后由模型查询一个具有更精确的子图的子图,我们使用更高级的图, 将一个更高级的变的图显示一个更精确的亚图,然后用一个更精确的亚图,用来显示一个更精确的变的图。