Precise tooth instance segmentation from dental CBCT images is essential for accurate diagnosis, yet the scarcity of labeled data and the complex geometric variations of teeth make this task challenging. To address these issues, we propose ConsMatch, a semi-supervised framework that explicitly integrates geometric information into the learning process. It establishes task-level consistency between instance segmentation and boundary extraction, guiding the model to capture finegrained geometric structures. Furthermore, two geometry-aware strategies-Threshold Adjustment Strategy (TAS) and Weight Adjustment Strategy (WAS)-dynamically refine pseudo-label generation by adapting class-specific thresholds and supervision weights based on geometric consistency. This enables the model to focus on high-confidence, structure-consistent pseudolabels, enhancing training stability and segmentation accuracy. Experimental results on dental CBCT data show that ConsMatch achieves superior performance across Dice, Jaccard, and HD95 metrics, consistently outperforming existing semisupervised methods.

Designing a functional occlusal surface for denture crowns is a complex and important task in prosthodontics. Manual design is time-consuming and heavily relies on the dentist's experience, as it requires careful consideration of occlusal function. Due to the limitations of manual design, the field has turned to data-driven methods for occlusal surface design. However, many of these methods neglect critical geometric details, such as normals and curvature, impacting the quality of the occlusal surface. In this paper, we introduce Diff-OSGN, a novel denture crown occlusal surface generation network based on a denoising diffusion model, which focuses on generating the detailed geometric structure of denture crowns. We model the occlusal surface as a geometry map based on the occlusal plane, incorporating height and normal maps rasterized from intra-oral crown scanning. Both maps represent occlusal surface geometry, and their combination further enhances these details. Considering the crucial occlusal information, we extract features from the geometry maps of adjacent and occlusal teeth, using them as conditions in the reverse diffusion process to train our network for optimal occlusal function. Additionally, we define three geometric operators and corresponding loss functions as constraints to better extract geometric features of the target occlusal surface, such as ridges and grooves, for adequate supervision. Our results demonstrate that Diff-OSGN provides quantitatively and qualitatively superior performance than competing baselines and state-of-the-art methods.

Automated classification of skin lesions has been proven to improve the diagnostic accuracy of dermoscopic images significantly. Despite numerous achievements in this field, accurate classification remains challenging due to the diversity of skin cancer lesions and the class imbalance within datasets. Traditional methods typically augment imbalanced datasets by increasing perturbations to input data, but the improvements are often limited. Therefore, this paper proposes a method that utilizes genetic feature augmentation and class activation mapping (CAM) consistency for enhanced classification of imbalanced skin lesion data. The proposed genetic feature module incorporates the concept of genetic algorithms into the network model. The model can learn more diverse expressions by selectively retaining highly representative features while cross-replicating data from underrepresented classes. Additionally, we introduce a global lesion localization module based on CAM to enhance the learning of discriminative features among classes. This module optimizes the multiple CAM distances generated from the same dermoscopic image, thereby improving the differentiation of inter-class features. To validate the effectiveness of this method, extensive experiments were conducted on the ISIC-2017 and ISIC-2018 datasets. The experimental results demonstrate that this method performs exceptionally well on multi-class data, significantly improving classification accuracy and stability.

Computed tomography (CT) images are the most commonly used radiographic imaging modality for detecting and diagnosing lumbar diseases. Despite many outstanding advances, computer-aided diagnosis (CAD) of lumbar disc disease remains challenging due to the complexity of pathological abnormalities and poor discrimination between different lesions. Therefore, we propose a Collaborative Multi-Metadata Fusion classification network (CMMF-Net) to address these challenges. The network consists of a feature selection model and a classification model. We propose a novel Multi-scale Feature Fusion (MFF) module that can improve the edge learning ability of the network region of interest (ROI) by fusing features of different scales and dimensions. We also propose a new loss function to improve the convergence of the network to the internal and external edges of the intervertebral disc. Subsequently, we use the ROI bounding box from the feature selection model to crop the original image and calculate the distance features matrix. We then concatenate the cropped CT images, multiscale fusion features, and distance feature matrices and input them into the classification network. Next, the model outputs the classification results and the class activation map (CAM). Finally, the CAM of the original image size is returned to the feature selection network during the upsampling process to achieve collaborative model training. Extensive experiments demonstrate the effectiveness of our method. The model achieved 91.32% accuracy in the lumbar spine disease classification task. In the labelled lumbar disc segmentation task, the Dice coefficient reaches 94.39%. The classification accuracy in the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) reaches 91.82%.

Most community detection methods focus on the similarities between detection nodes to achieve community partitioning. Traditional network representation learning methods are also limited to the local context of the central nodes, which results in less truly representative results. This article examines nodes' influence information, nodes' community affiliating information, and similarity of community topologies and proposes a more effective node representation strategy. According to the local node information and global topology in the social network graph, a method of combining local node embedding and global community embedding is also designed. The effectiveness of learning node representation and community representation is improved by our approach. The proposed model can also effectively detect overlapping communities.