  Deep learning model trained by imbalanced data may not work satisfactorily
since it could be determined by major classes and thus may ignore the classes
with small amount of data. In this paper, we apply deep learning based
imbalanced data classification for the first time to cellular macromolecular
complexes captured by Cryo-electron tomography (Cryo-ET). We adopt a range of
strategies to cope with imbalanced data, including data sampling, bagging,
boosting, Genetic Programming based method and. Particularly, inspired from
Inception 3D network, we propose a multi-path CNN model combining focal loss
and mixup on the Cryo-ET dataset to expand the dataset, where each path had its
best performance corresponding to each type of data and let the network learn
the combinations of the paths to improve the classification performance. In
addition, extensive experiments have been conducted to show our proposed method
is flexible enough to cope with different number of classes by adjusting the
number of paths in our multi-path model. To our knowledge, this work is the
first application of deep learning methods of dealing with imbalanced data to
the internal tissue classification of cell macromolecular complexes, which
opened up a new path for cell classification in the field of computational
biology.
