Shuyao He

Jinyue Li, Yuzhou Yu, Jingjing Yang et al.

The accurate classification of benign and malignant pulmonary nodules in CT scans is critical for early lung cancer screening, yet remains challenging due to the multi-scale and heterogeneous nature of pulmonary nodules. While deep learning offers potential for auxiliary diagnosis, most existing models act as "black boxes", lacking the transparency and explainability required for trustworthy clinical integration. To address this issue, we propose M3Net, a novel 3D network for pulmonary nodule classification inspired by the hierarchical diagnostic workflow of radiologists, which integrates multi-scale contextual information from fine-grained structures to global anatomical relationships. Our framework constructs a progressive multi-scale input, from fine-grained nodule structures to local semantics and global spatial relationships. M3Net employs scale-specific encoders and ensures cross-scale semantic consistency through latent space projection and mutual information maximization. Extensive experiments on the public LIDC-IDRI dataset and a self-collected clinical dataset (USTC-FHLN) demonstrate that our method achieves state-of-the-art performance, with accuracies of 86.96% and 84.24% respectively, outperforming the best baseline by 3.26% and 2.17%. The results validate that M3Net provides a more robust and clinically relevant solution for pulmonary nodule classification. The code is available at https://github.com/jylEcho/M3-Net.

Haowei Yang, Yuxiang Hu, Shuyao He et al.

This study introduces an innovative application of Conditional Generative Adversarial Networks (C-GAN) integrated with Stacked Hourglass Networks (SHGN) aimed at enhancing image segmentation, particularly in the challenging environment of medical imaging. We address the problem of overfitting, common in deep learning models applied to complex imaging datasets, by augmenting data through rotation and scaling. A hybrid loss function combining L1 and L2 reconstruction losses, enriched with adversarial training, is introduced to refine segmentation processes in intravascular ultrasound (IVUS) imaging. Our approach is unique in its capacity to accurately delineate distinct regions within medical images, such as tissue boundaries and vascular structures, without extensive reliance on domain-specific knowledge. The algorithm was evaluated using a standard medical image library, showing superior performance metrics compared to existing methods, thereby demonstrating its potential in enhancing automated medical diagnostics through deep learning

Yuxiang Hu, Haowei Yang, Ting Xu et al.

The diagnosis of brain cancer relies heavily on medical imaging techniques, with MRI being the most commonly used. It is necessary to perform automatic segmentation of brain tumors on MRI images. This project intends to build an MRI algorithm based on U-Net. The residual network and the module used to enhance the context information are combined, and the void space convolution pooling pyramid is added to the network for processing. The brain glioma MRI image dataset provided by cancer imaging archives was experimentally verified. A multi-scale segmentation method based on a weighted least squares filter was used to complete the 3D reconstruction of brain tumors. Thus, the accuracy of three-dimensional reconstruction is further improved. Experiments show that the local texture features obtained by the proposed algorithm are similar to those obtained by laser scanning. The algorithm is improved by using the U-Net method and an accuracy of 0.9851 is obtained. This approach significantly enhances the precision of image segmentation and boosts the efficiency of image classification.

Jiajie Yuan, Linxiao Wu, Yulu Gong et al.

This paper combines Struts and Hibernate two architectures together, using DAO (Data Access Object) to store and access data. Then a set of dual-mode humidity medical image library suitable for deep network is established, and a dual-mode medical image assisted diagnosis method based on the image is proposed. Through the test of various feature extraction methods, the optimal operating characteristic under curve product (AUROC) is 0.9985, the recall rate is 0.9814, and the accuracy is 0.9833. This method can be applied to clinical diagnosis, and it is a practical method. Any outpatient doctor can register quickly through the system, or log in to the platform to upload the image to obtain more accurate images. Through the system, each outpatient physician can quickly register or log in to the platform for image uploading, thus obtaining more accurate images. The segmentation of images can guide doctors in clinical departments. Then the image is analyzed to determine the location and nature of the tumor, so as to make targeted treatment.

Yafeng Yan, Shuyao He, Zhou Yu et al.

Aiming at the limitations of traditional medical decision system in processing large-scale heterogeneous medical data and realizing highly personalized recommendation, this paper introduces a personalized medical decision algorithm utilizing graph neural network (GNN). This research innovatively integrates graph neural network technology into the medical and health field, aiming to build a high-precision representation model of patient health status by mining the complex association between patients' clinical characteristics, genetic information, living habits. In this study, medical data is preprocessed to transform it into a graph structure, where nodes represent different data entities (such as patients, diseases, genes, etc.) and edges represent interactions or relationships between entities. The core of the algorithm is to design a novel multi-scale fusion mechanism, combining the historical medical records, physiological indicators and genetic characteristics of patients, to dynamically adjust the attention allocation strategy of the graph neural network, so as to achieve highly customized analysis of individual cases. In the experimental part, this study selected several publicly available medical data sets for validation, and the results showed that compared with traditional machine learning methods and a single graph neural network model, the proposed personalized medical decision algorithm showed significantly superior performance in terms of disease prediction accuracy, treatment effect evaluation and patient risk stratification.