Accurate 3D reconstruction of the small bowel skeleton is vital for understanding intestinal morphology, de-tecting structural abnormalities, and supporting diagnosis, yet limited resolution, organ adhesion, complex anatomy, and scarce annotations make continuous skeleton extraction from masks challenging. Voxel-based methods often struggle with the sparse topology and geometric directionality inherent in the small bowel skeleton, leading to inefficiency and high memory cost. To address these limitations, we propose a novel octree-based conditional diffusion model (i.e., Tree-Diffusion) that generates anatomically consistent small bowel skeletons guided by 3D segmentation masks. Specifically, we introduce two modules that captures structural priors from masks and topology characteristics from skeletons, ensuring cross-domain alignment and high-quality skeleton generation. Besides, we design a synthesis strategy to generate anatomically plausible skeleton-mask pairs, serving as topological priors to guide the diffusion model toward realis-tic structure predictions. To efficiently represent the elongated skeleton, we adopt an octree- based spatial encoding of hierarchical geometric features. Compared with baselines, our model achieves superior performance in anatomical fidelity, directional consistency, and inference efficiency. The code is available at: https://github.com/Small-Bowel-Skeleton-GenerationlCode

Breast cancer poses a significant threat to women's health, and ultrasound plays a critical role in the assessment of breast lesions. This study introduces a prospective deep learning architecture, termed the "Multi-modal Multi-task Network" (3MT-Net), which integrates clinical data with B-mode and color Doppler ultrasound images. Specifically, an AM-CapsNet is employed to extract key features from ultrasound images, while a cascaded cross-attention mechanism is utilized to fuse clinical data. Moreover, an ensemble learning approach with an optimization algorithm is adopted to dynamically assign weights to different modalities, accommodating both high-dimensional and low-dimensional data. The 3MT-Net performs binary classification of benign versus malignant lesions and further classifies the pathological subtypes. Data were retrospectively collected from nine medical centers to ensure the broad applicability of the 3MT-Net. Two separate testsets were created and extensive experiments were conducted. Comparative analyses demonstrated that the AUC of the 3MT-Net outperforms the industry-standard computer-aided detection product, S-Detect, by 1.4% to 3.8%.

Heart disease is a common condition worldwide and has become one of the leading causes of death worldwide. The electrocardiogram (PCG) is a safe, painless, and non-invasive test that captures bioacoustic information reflecting the function of the heart by capturing the acoustic signal of the patient’s heart. Nowadays, based on biosignal processing and artificial intelligence technologies, automated heart sound classification is playing an increasingly important role in clinical applications. In this paper, we propose a new parallel CNNs-transformer network with multi-scale feature context aggregation (PCTMF-Net). It combines the advantages of CNNs and transformer to achieve efficient heart sound classification. In PCTMF-Net, firstly, the heart tone signal features are extracted using the second-order spectral analysis, and a transformer-based MHTE-4 (multi-head transformer encoder with four attention heads) is designed to encode and aggregate the contextual information, and then, two CNNs feature extractors are designed in parallel with MHTE-4 to capture the hierarchical features. Finally, the feature vectors obtained from CNNs and MHTE-4 through feature fusion in PCTMF-Net will be fed into the fully connected layer for predicting the classification results of heart sounds. In addition, we perform validation based on two publicly available mutually exclusive heart sound datasets and conduct extensive experiments and comparisons of existing algorithms under different metrics. The experimental results show that our proposed method achieves 99.36% accuracy on the Yaseen dataset and 93% accuracy on the PhysioNet dataset. It surpasses current algorithms in terms of accuracy, recall and F 1-score metrics. The aim of this study is to apply these new techniques and methods to improve the diagnostic accuracy and validity of heart disease for clinical use.