  The rapid development in representation learning techniques such as deep
neural networks and the availability of large-scale, well-annotated medical
imaging datasets have to a rapid increase in the use of supervised machine
learning in the 3D medical image analysis and diagnosis. In particular, deep
convolutional neural networks (D-CNNs) have been key players and were adopted
by the medical imaging community to assist clinicians and medical experts in
disease diagnosis and treatment. However, training and inferencing deep neural
networks such as D-CNN on high-resolution 3D volumes of Computed Tomography
(CT) scans for diagnostic tasks pose formidable computational challenges. This
challenge raises the need of developing deep learning-based approaches that are
robust in learning representations in 2D images, instead 3D scans. In this
work, we propose for the first time a new strategy to train \emph{slice-level}
classifiers on CT scans based on the descriptors of the adjacent slices along
the axis. In particular, each of which is extracted through a convolutional
neural network (CNN). This method is applicable to CT datasets with per-slice
labels such as the RSNA Intracranial Hemorrhage (ICH) dataset, which aims to
predict the presence of ICH and classify it into 5 different sub-types. We
obtain a single model in the top 4% best-performing solutions of the RSNA ICH
challenge, where model ensembles are allowed. Experiments also show that the
proposed method significantly outperforms the baseline model on CQ500. The
proposed method is general and can be applied to other 3D medical diagnosis
tasks such as MRI imaging. To encourage new advances in the field, we will make
our codes and pre-trained model available upon acceptance of the paper.
