  Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables
the visualization of subcellular structures in situ at near-atomic resolution.
Cellular cryo-ET images help in resolving the structures of macromolecules and
determining their spatial relationship in a single cell, which has broad
significance in cell and structural biology. Subtomogram classification and
recognition constitute a primary step in the systematic recovery of these
macromolecular structures. Supervised deep learning methods have been proven to
be highly accurate and efficient for subtomogram classification, but suffer
from limited applicability due to scarcity of annotated data. While generating
simulated data for training supervised models is a potential solution, a
sizeable difference in the image intensity distribution in generated data as
compared to real experimental data will cause the trained models to perform
poorly in predicting classes on real subtomograms. In this work, we present
Cryo-Shift, a fully unsupervised domain adaptation and randomization framework
for deep learning-based cross-domain subtomogram classification. We use
unsupervised multi-adversarial domain adaption to reduce the domain shift
between features of simulated and experimental data. We develop a
network-driven domain randomization procedure with `warp' modules to alter the
simulated data and help the classifier generalize better on experimental data.
We do not use any labeled experimental data to train our model, whereas some of
the existing alternative approaches require labeled experimental samples for
cross-domain classification. Nevertheless, Cryo-Shift outperforms the existing
alternative approaches in cross-domain subtomogram classification in extensive
evaluation studies demonstrated herein using both simulated and experimental
data.
