  In medical-data driven learning, 3D convolutional neural networks (CNNs) have
started to show superior performance to 2D CNNs in numerous deep learning
tasks, proving the added value of 3D spatial information in feature
representation. However, the difficulty in collecting more training samples to
converge, more computational resources and longer execution time make this
approach less applied. Also, applying transfer learning on 3D CNN is
challenging due to a lack of publicly available pre-trained 3D models. To
tackle these issues, we proposed a novel 2D strategical representation of
volumetric data, namely 2.75D. In this work, the spatial information of 3D
images is captured in a single 2D view by a spiral-spinning technique. As a
result, 2D CNN networks can also be used to learn volumetric information.
Besides, we can fully leverage pre-trained 2D CNNs for downstream vision
problems. We also explore a multi-view 2.75D strategy, 2.75D 3 channels
(2.75Dx3), to boost the advantage of 2.75D. We evaluated the proposed methods
on three public datasets with different modalities or organs (Lung CT, Breast
MRI, and Prostate MRI), against their 2D, 2.5D, and 3D counterparts in
classification tasks. Results show that the proposed methods significantly
outperform other counterparts when all methods were trained from scratch on the
lung dataset. Such performance gain is more pronounced with transfer learning
or in the case of limited training data. Our methods also achieved comparable
performance on other datasets. In addition, our methods achieved a substantial
reduction in time consumption of training and inference compared with the 2.5D
or 3D method.
