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Three-dimensional convolutional neural networks (3DCNNs), a rapidly evolving modality of deep learning, has gained popularity in many fields. For oral cancers, CT images are traditionally processed using two-dimensional input, without considering information between lesion slices. In this paper, we established a 3DCNNs-based image processing algorithm for the early diagnosis of oral cancers, which was compared with a 2DCNNs-based algorithm. The 3D and 2D CNNs were constructed using the same hierarchical structure to profile oral tumors as benign or malignant. Our results showed that 3DCNNs with dynamic characteristics of the enhancement rate image performed better than 2DCNNS with single enhancement sequence for the discrimination of oral cancer lesions. Our data indicate that spatial features and spatial dynamics extracted from 3DCNNs may inform future design of CT-assisted diagnosis system. INDEX TERMS 2DCNNs, 3DCNNs, CT images, spatial features, spatial dynamics extracted.

Prostate cancer is one of the most prevalent cancers among men. Early detection of this cancer could effectively increase the survival rate of the patient. In this paper, we propose a Bi-attention adversarial network for the prostate cancer segmentation automatically. The proposed architecture consists of the generator network and discriminator network. The generator network aims to generate the predicted mask of the input image, while the discriminator network aims to further improve the generator performance with adversarial learning by discriminating the generator predicted mask and the true label mask. For better improving the segmentation performance, we combine two attention mechanisms with the generator network to learn more global and local features. Extensive experiments on the T2-weighted (T2W) images have demonstrated our model could achieve state-of-the-art segmentation performance compared with other methods.

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The performance of composite ceramic armor penetrated by rod projectile was studied by both numerical simulation and experiment. The penetration and damage mechanisms of the projectile-armor after high-speed collision were also observed by high-speed photography. The experimental results showed that the ballistic performance of composite ceramic armor was highly affected by the density, hardness and toughness of bulletproof ceramic. The flow stress of the failed bulletproof ceramic is not only related to the pressure but also related to the strain rate. The phenomenological method based on Bodner-Partom ceramic model was introduced to derive the growth rate of damage. Numerical simulation results show good agreement with the experimental results.