  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.
