Accurate retinal blood vessels segmentation is an important step in the clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and uncertain pathological features make blood vessel segmentation still very challenging. This paper proposes a novel multimodule concatenation via a U-shaped network for retinal vessels segmentation, which is based on atrous convolution and multikernel pooling. The proposed network structure retains three layers of the essential structure of U-Net, in which the atrous convolution combining the multikernel pooling blocks are designed to obtain more contextual information. The spatial attention module is concatenated with the dense atrous convolution module and the multikernel pooling module to form a multimodule concatenation. And different dilation rates are selected by cascading to acquire a larger receptive field in atrous convolution. Adequate comparative experiments are conducted on these public retinal datasets: DRIVE, STARE, and CHASE_DB1. The results show that the proposed method is effective, especially for microvessels. The code will be released at https://github.com/rocklijun/MC-UNet.

Inflammatory bowel disease (IBD) is a global disease that is evolving with increasing incidence. However, there are few works on computationally assisted diagnosis of IBD based on pathological images. Therefore, based on the UK and Chinese IBD diagnostic guidelines, our study established an artificial intelligence-assisted diagnostic system for histologic grading of inflammatory activity in ulcerative colitis (UC). We proposed an efficient deep-learning (DL) method for grading inflammatory activity in whole-slide images (WSIs) of UC pathology. Our model was constructed using 603 UC WSIs from Nanjing Drum Tower Hospital for model train set and internal test set. We collected 212 UC WSIs from Zhujiang Hospital as an external test set. Initially, the pre-trained ResNet50 model on the ImageNet dataset was employed to extract image patch features from UC patients. Subsequently, a multi-instance learning (MIL) approach with embedded self-attention was utilized to aggregate tissue image patch features, representing the entire WSI. Finally, the model was trained based on the aggregated features and WSI annotations provided by senior gastrointestinal pathologists to predict the level of inflammatory activity in UC WSIs. In the task of distinguishing the presence or absence of inflammatory activity, the Area Under Curve (AUC) value in the internal test set is 0.863 (95% confidence interval [CI] 0.829, 0.898), with a sensitivity of 0.913 (95% [CI] 0.866, 0.961), and specificity of 0.816 (95% [CI] 0.771, 0.861). The AUC in the external test set is 0.947 (95% confidence interval [CI] 0.939, 0.955), with a sensitivity of 0.889 (905% [CI] 0.837, 0.940), and specificity of 0.858 (95% [CI] 0.777, 0.939). For distinguishing different levels of inflammatory activity in UC, the average Macro-AUC in the internal test set and the external test set are 0.827 (95% [CI] 0.803, 0.850) and 0.908 (95% [CI] 0.882, 0.935). the average Micro-AUC in the internal test set and the external test set are 0.816 (95% [CI] 0.792, 0.840) and 0.898 (95% [CI] 0.869, 0.926). Comparative analysis with diagnoses made by pathologists at different expertise levels revealed that the algorithm reached a proficiency comparable to the pathologist with 5 years of experience. Furthermore, our algorithm performed superior to other MIL algorithms.

Whole-slide images (WSIs) pose significant analytical challenges due to their large data scale and complexity. Multiple instance learning (MIL) has emerged as an effective solution for WSI classification, but existing frameworks often lack flexibility in feature integration and underutilize sequential information. To address these limitations, this work proposes a novel MIL framework: Dynamic Graph and State Space Model-Based MIL (DG-SSM-MIL). DG-SSM-MIL combines graph neural networks and selective state space models, leveraging the former’s ability to extract local and spatial features and the latter’s advantage in comprehensively understanding long-sequence instances. This enhances the model’s performance in diverse instance classification, improves its capability to handle long-sequence data, and increases the precision and scalability of feature fusion. We propose the Dynamic Graph and State Space Model (DynGraph-SSM) module, which aggregates local and spatial information of image patches through directed graphs and learns global feature representations using the Mamba model. Additionally, the directed graph structure alleviates the unidirectional scanning limitation of Mamba and enhances its ability to process pathological images with dispersed lesion distributions. DG-SSM-MIL demonstrates superior performance in classification tasks compared to other models. We validate the effectiveness of the proposed method on features extracted from two pretrained models across four public medical image datasets: BRACS, TCGA-NSCLC, TCGA-RCC, and CAMELYON16. Experimental results demonstrate that DG-SSM-MIL consistently outperforms existing MIL methods across four public datasets. For example, when using ResNet-50 features, our model achieves the highest AUCs of 0.936, 0.785, 0.879, and 0.957 on TCGA-NSCLC, BRACS, CAMELYON16, and TCGA-RCC, respectively. Similarly, with UNI features, DG-SSM-MIL reaches AUCs of 0.968, 0.846, 0.993, and 0.990, surpassing all baselines. These results confirm the effectiveness and generalizability of our approach in diverse WSI classification tasks.