Segmenting stroke lesions automatically can provide valuable support to the clinical decision process. However, this is a challenging task due to the diversity of lesion size, shape, and location. Previous works have failed to capture global context information which is helpful to handle the diversity. To solve the problem of segmentation of ischemic stroke lesions with small sample size, an end-to-end neural network combing with residual block and non-local block on the basis of traditional U-Net was proposed to predict stroke lesion from multi-modal Magnetic Resonance Imaging (MRI) image. In this method, based on the encoder-decoder architecture of U-Net, residual blocks were stacked to solve the degradation problem and avoid the overfitting, and the non-local blocks were added to effectively encode the long-distance dependencies and provide global context information for the feature extraction process. The proposed method and its variants were evaluated on the Ischemic Stroke Lesion Segmentation (ISLES) 2017 dataset. The results showed that the proposed residual U-Net (Dice=0.29±0.23, ASSD=7.66±6.41, HD=43.71±22.11) and Residual Non-local U-Net (RN-UNet) (Dice=0.29±0.23, ASSD=7.61±6.62, HD=45.36±24.75) achieved significant improvement in all metrics compared to the baseline U-Net (Dice=0.25±0.23, ASSD=9.45±7.36, HD=54.59 ±21.19); compared with the state-of-the-art methods from ISLES website, the two methods both achieved better segmentation results, so that they can help doctors to quickly and objectively evaluate the condition of patients in clinical practices.

The histopathological image is the gold standard for identifying breast cancer, so that the automatic and accurate classification of breast cancer histopathological images is of great clinical application. In order to improve the classification accuracy of breast cancer histopathology images and thus meet the needs of clinical applications, a high-precision breast classification method that incorporates spatial and channel features was proposed. In the method, the histopathological images were processed by using color normalization and the dataset was expanded by using data enhancement, and the spatial feature information and channel feature information of the histopathological images were fused based on the Convolutional Neural Network (CNN) models DenseNet and Squeeze-and-Excitation Network (SENet). Three different BCSCNet (Breast Classification fusing Spatial and Channel features Network) models, BCSCNetⅠ, BCSCNetⅡ and BCSCNetⅢ, were designed according to the insertion position and the number of Squeeze-and-Excitation (SE) modules. The experiments were carried out on the breast cancer histopathology image dataset (BreaKHis), and through experimental comparison, it was firstly verified that color normalization and data enhancement of the images were able to improve the classification accuracy of breast canner, and then among the three designed breast canner classification models, the one with the highest precision was found to be BCSCNetⅢ. Experimental results showed that BCSCNetⅢ had the accuracy of binary classification ranged from 99.05% to 99.89%, which was improved by 0.42 percentage points compared with Breast cancer Histopathology image Classification Network (BHCNet); and the accuracy of multi-class classification ranged from 93.06% to 95.72%, which was improved by 2.41 percentage points compared with BHCNet. It proves that BCSCNet can accurately classify breast cancer histopathological images and provide reliable theoretical support for computer-aided breast cancer diagnosis.