The stage prediction of kidney renal clear cell carcinoma (KIRC) is important for the diagnosis, personalized treatment, and prognosis of patients. Many prediction methods have been proposed, but most of them are based on unimodal gene data, and their accuracy is difficult to further improve. Therefore, we propose a novel multi-weighted dynamic cascade forest based on the bilinear feature extraction (MLW-BFECF) model for stage prediction of KIRC using multimodal gene data (RNA-seq, CNA, and methylation). The proposed model utilizes a dynamic cascade framework with shuffle layers to prevent early degradation of the model. In each cascade layer, a voting technique based on three gene selection algorithms is first employed to effectively retain gene features more relevant to KIRC and eliminate redundant information in gene features. Then, two new bilinear models based on the gated attention mechanism are proposed to better extract new intra-modal and inter-modal gene features; Finally, based on the idea of the bagging, a multi-weighted ensemble forest classifiers module is proposed to extract and fuse probabilistic features of the three-modal gene data. A series of experiments demonstrate that the MLW-BFECF model based on the three-modal KIRC dataset achieves the highest prediction performance with an accuracy of 88.9 %.

In remote sensing images, roads are usually in complex shapes and can be partially occluded by buildings, trees, and other surroundings. To extract a complete and continuous road network is still a challenging job. This paper proposes a context-aware road extraction method for remote sensing imagery based on Transformer network, in which, a foreground feature enhancement module (FFEM) is designed to further extract detailed road features such as contours from the shallowest feature map; Dual-attention module (DAM) is constructed and applied at different skip connections to make the model focus more on road features in the different level of feature maps; A Swin Transformer-based contextual information extraction module (CIEM) is built between the encoder and decoder modules to capture the global and local road contextual information so as to recover the occluded roads information as much as possible. Furthermore, a multi-scale decoder (M-Decoder) is designed to improve the feature map recovery ability of the decoder module. Experiments on the DeepGlobe road dataset are conducted to verify the efficiency of the proposed method.