We proposed the LR relationship-aware classification model of 3D volume. The proposed model effectively extracts image features from LR symmetric positions. The multi-shift symmetric feature extraction module was employed to accommodate small positional gaps among LR corresponding positions. The experimental results of 3D volume classification tasks of the lung and brain showed that the proposed method achieved superior performances compared to the previous models. Our code is available at https://github.com/modafone/lr3dvolumeclassification .
Oda et al. (Wed,) studied this question.