  Motivation: Cryo-Electron Tomography (cryo-ET) visualizes structure and
spatial organization of macromolecules and their interactions with other
subcellular components inside single cells in the close-to-native state at
sub-molecular resolution. Such information is critical for the accurate
understanding of cellular processes. However, subtomogram classification
remains one of the major challenges for the systematic recognition and recovery
of the macromolecule structures in cryo-ET because of imaging limits and data
quantity. Recently, deep learning has significantly improved the throughput and
accuracy of large-scale subtomogram classification. However often it is
difficult to get enough high-quality annotated subtomogram data for supervised
training due to the enormous expense of labeling. To tackle this problem, it is
beneficial to utilize another already annotated dataset to assist the training
process. However, due to the discrepancy of image intensity distribution
between source domain and target domain, the model trained on subtomograms in
source domainmay perform poorly in predicting subtomogram classes in the target
domain.
  Results: In this paper, we adapt a few shot domain adaptation method for deep
learning based cross-domain subtomogram classification. The essential idea of
our method consists of two parts: 1) take full advantage of the distribution of
plentiful unlabeled target domain data, and 2) exploit the correlation between
the whole source domain dataset and few labeled target domain data. Experiments
conducted on simulated and real datasets show that our method achieves
significant improvement on cross domain subtomogram classification compared
with baseline methods.
