A stereo spatial decoupling network for medical image classification.

Deep convolutional neural network (CNN) has made great progress in medical image classification. However, it is difficult to establish effective spatial associations, and always extracts similar low-level features, resulting in redundancy of information. To solve these limitations, we propose a stereo spatial discoupling network (TSDNets), which can leverage the multi-dimensional spatial details of medical images. Then, we use an attention mechanism to progressively extract the most discriminative features from three directions: horizontal, vertical, and depth. Moreover, a cross feature screening strategy is used to divide the original feature maps into three levels: important, secondary and redundant. Specifically, we design a cross feature screening module (CFSM) and a semantic guided decoupling module (SGDM) to model multi-dimension spatial relationships, thereby enhancing the feature representation capabilities. The extensive experiments conducted on multiple open source baseline datasets demonstrate that our TSDNets outperforms previous state-of-the-art models.

Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks.

The diversification of the characteristic sequences of anti-cancer peptides has imposed difficulties on research. To effectively predict new anti-cancer peptides, this paper proposes a more suitable feature grouping sequence and spatial dimension-integrated network algorithm for anti-cancer peptide sequence prediction called GRCI-Net. The main process is as follows: First, we implemented the fusion reduction of binary structure features and K-mer sparse matrix features through principal component analysis and generated a set of new features; second, we constructed a new bidirectional long- and short-term memory network. We used traditional convolution and dilated convolution to acquire features in the spatial dimension using the memory network's grouping sequence model, which is designed to better handle the diversification of anti-cancer peptide feature sequences and to fully learn the contextual information between features. Finally, we achieved the fusion of grouping sequence features and spatial dimensional integration features through two sets of dense network layers, achieved the prediction of anti-cancer peptides through the sigmoid function, and verified the approach with two public datasets, ACP740 (accuracy reached 0.8230) and ACP240 (accuracy reached 0.8750). The following is a link to the model code and datasets mentioned in this article: https://github.com/ YouHongfeng101/ACP-DL.