Deep learning has significantly advanced medical image processing, yet the inherent inclusion of personally identifiable information (PII) within medical images-such as facial features, distinctive anatomical structures, rare lesions, or specific textural patterns-poses a critical risk to patient privacy during data transmission. To mitigate this risk, we introduce the Medical Semantic Diffusion Model (MSDM), a novel framework designed to synthesize medical images guided by semantic information, synthesis images with the same distribution as the original data, which effectively removes the PPI of the original data to ensure robust privacy protection. Unlike conventional techniques that combine semantic and noisy images for denoising, MSDM integrates Adaptive Batch Normalization (AdaBN) to encode semantic information into high-dimensional latent space, embedding it directly within the denoising neural network. This approach enhances image quality and semantic accuracy while ensuring that the synthetic and original images belong to the same distribution. In addition, to further accelerate synthesis and reduce dependency on manually crafted semantic masks, we propose the Spread Algorithm, which automatically generates these masks. Extensive experiments conducted on the BraTS 2021, MSD Lung, DSB18, and FIVES datasets confirm the efficacy of MSDM, yielding state-of-the-art results across several performance metrics. Augmenting datasets with MSDM-generated images in nnUNet segmentation experiments led to Dice scores of 0.6243, 0.9531, 0.9406, and 0.9562 underscoring its potential for enhancing both image quality and privacy-preserving data augmentation.

Medical image segmentation is a crucial technology for advancing smart healthcare, yet its performance is hampered by limited model generalization due to scarce annotated data. Current solutions face significant challenges: transfer learning from natural images struggles to adapt effectively to medical image feature distributions owing to domain gaps, while mainstream semi-supervised co-training frameworks (e.g., Cross Pseudo Supervision, CPS) suffer from premature consensus among sub-networks, diminishing the utility of unlabeled data. To address this, we propose ReConCPS, an enhanced co-training framework integrating feature reconstruction and a dual perturbation mechanism. First, to mitigate transfer learning difficulties, we introduce an unsupervised feature reconstruction task as an auxiliary branch in the pre-trained encoder, leveraging unlabeled data to guide the learning of more discriminative medical image features. Second, targeting CPS's premature convergence, we devise a task perturbation strategy: the main network concurrently performs segmentation and reconstruction, while the auxiliary network focuses solely on segmentation, thereby establishing behavioral discrepancy through objective divergence. Finally, we implement a dual perturbation mechanism-applying feature-space perturbations to enhance robustness and employing heterogeneous encoder architectures (structural perturbation) for the two sub-networks-collectively promoting diverse feature learning. Experiments on public medical image datasets demonstrate the superiority of ReConCPS in addressing annotation scarcity and enhancing model generalization.

KAN-based networks, while offering improved interpretability compared to traditional models used in medical image segmentation, often struggle with limited adaptability to diverse imaging environments, making them less ideal for such tasks. To address this issue, we propose a Fourier Kolmogorov–Arnold–based (FKAN) Group Multi–scale Fusion Network, termed FKAN–GMFNet, which incorporates an FKAN layer into a labeled intermediate representation, introducing an Enhanced–FKAN block. Furthermore, we develop the Attention Group Multi–scale Aggregation (ATGMA) module, which leverages attention mechanisms and grouping strategies to effectively fuse feature masks with both high– and low–scale feature information, thereby achieving a comprehensive multi-scale feature representation. Extensive experiments demonstrate that the FKAN GMFNet significantly outperforms seven state–of–the–art methods in both Dice and IoU scores, where the Dice and IoU scores for the IAS–L dataset are 88.82% and 80.09%, respectively. Code is available at https://github.com/zx123868/FKAN-GMFNet.

In medical imaging, segmentation of tissues and organs helps doctors accurately determine disease subtypes and propose treatment plans. Few-shot learning, which can be trained with a small amount of annotated data and has better generalization, has achieved excellent results in medical image segmentation, where large-scale annotated data is scarce. However, most current few-shot segmentation models rely on the foreground part of support features, establishing spatial information connections with query features through the foreground part and ignoring the differences in foreground classes between query and support images. Therefore, we propose a novel few-shot segmentation model based on cross-attention: aligned patch calibration attention network (APCANet), which filters out pixels detrimental to segmentation based on cross-attention between support features and query features. Specifically, our model is patch-based, setting the query patches as Query and the aligned support patches as Key and Value for cross-attention. Then, in the cross-attention matrix, by distinguishing the tokens in the patches, we set the weights of non-compliant tokens in the attention matrix to negative infinity, thereby calibrating the harmful pixels in the support features. Additionally, we generate a prior mask for the query image and concatenate it with the query features. Subsequently, we divide the support and query features into patches and align foreground and background patches based on similarity. These operations can better establish spatial correlation between support and query features. The experimental results on the Abd-CT, Card-MRI, and Abd-MRI datasets demonstrate the effectiveness of our method.Code is released at: https://github.com/HengTaoDing/APCANet

The provision of privacy-preserving recommendations for geological tourist attractions is an important research area. The historical check-in data collected from location-based social networks (LBSNs) can be utilized to mine their preferences, thereby facilitating the promotion of the geological tourism industry. However, such check-ins often contain sensitive user information that poses privacy leakage risks. To address this issue, some methods have been proposed to develop privacy-preserving point-of-interest (POI) recommendation systems. These methods commonly rely on either perturbation-based or federated learning techniques to protect users’ privacy. However, the former can hinder preference capture, while the latter remains vulnerable to privacy breaches during the parameter-sharing process. To overcome these challenges, we propose a novel privacy-preserving POI recommendation model that incorporates users’ privacy preferences based on a simplified graph convolutional neural network. Specifically, we employ a generative model to create a subset of POIs that reflect users’ preferences but do not reveal their private information, and then we design a simplified graph convolutional network to analyze the high-order connectivity between users and POIs that are privacy-preserving. The resulting model enables efficient POI recommendation under strict privacy protection, which is particularly relevant to geological tourism. Experimental results on two public datasets demonstrate the effectiveness of our proposed approach.

Echocardiography is essential for evaluating cardiac anatomy and function during early recognition and screening for congenital heart disease (CHD), a widespread and complex congenital malformation. However, fetal CHD recognition still faces many difficulties due to instinctive fetal movements, artifacts in ultrasound images, and distinctive fetal cardiac structures. These factors hinder capturing robust and discriminative representations from ultrasound images, resulting in CHD's low prenatal detection rate. Hence, we propose a multi-scale gated axial-transformer network (MSGATNet) to capture fetal four-chamber semantic information. Then, we propose a SPReCHD: four-chamber semantic parsing network for recognizing fetal CHD in the clinical treatment of the medical metaverse, integrating MSGATNet to segment and locate four-chamber arbitrary contours, further capturing distinguished representations for the fetal heart. Comprehensive experiments indicate that our SPReCHD is sufficient in recognizing fetal CHD, achieving a precision of 95.92%, a recall of 94%, an accuracy of 95%, and a$F_{1}$score of 94.95% on the test set, dramatically improving the fetal CHD's prenatal detection rate.

With the advancement of sequencing methodologies, the acquisition of vast amounts of multi-omics data presents a significant opportunity for comprehending the intricate biological mechanisms underlying diseases and achieving precise diagnosis and treatment for complex disorders. However, as diverse omics data are integrated, extracting sample-specific features within each omics modality and exploring potential correlations among different modalities while avoiding mutual interference becomes a critical challenge in multi-omics data integration research. In the context of this study, we proposed a framework that unites specificity-aware GATs and cross-modal attention to integrate different omics data (MOSGAT). To be specific, we devise Graph Attention Networks (GATs) tailored for each omics modality data to perform feature extraction on samples. Additionally, an adaptive confidence attention weighting technique is incorporated to enhance the confidence in the extracted features. Finally, a cross-modal attention mechanism was devised based on multi-head self-attention, thoroughly uncovering potential correlations between different omics data. Extensive experiments were conducted on four publicly available medical datasets, highlighting the superiority of the proposed framework when compared to state-of-the-art methodologies, particularly in the realm of classification tasks. The experimental results underscore MOSGAT's effectiveness in extracting features and exploring potential inter-omics associations.

There exists growing evidence that circRNAs are concerned with many complex diseases physiological processes and pathogenesis and may serve as critical therapeutic targets. Identifying disease-associated circRNAs through biological experiments is time-consuming, and designing an intelligent, precise calculation model is essential. Recently, many models based on graph technology have been proposed to predict circRNA-disease association. However, most existing methods only capture the neighborhood topology of the association network and ignore the complex semantic information. Therefore, we propose a Dual-view Edge and Topology Hybrid Attention model for predicting CircRNA-Disease Associations (DETHACDA), effectively capturing the neighborhood topology and various semantics of circRNA and disease nodes in a heterogeneous network. The 5-fold cross-validation experiments on circRNADisease indicate that the proposed DETHACDA achieves the area under receiver operating characteristic curve of 0.9882, better than four state-of-the-art calculation methods.

In recent years, Convolutional Neural Neural Networks (CNNs) and Transformer architectures have significantly advanced the field of medical image segmentation. Since CNNs can only obtain effective local feature representations, there is difficulty in establishing long-range dependencies. However, Transformer has gained extensive attention from researchers due to its powerful global context modeling capability. Therefore, to integrate the advantages of the two architectures, we propose a network of ConTNet that can combine local and global information, consisting of two parallel encoders, namely, the Transformer and the CNN encoder. The CNN encoder is a stack of deep convolution and Criss-cross attention module (CCAM), which aims to acquire local features while strengthening the connection with the surrounding pixel points. In addition, two different forms of features are fused and fed into the encoder to ensure semantic consistency. Extensive experiments on the aneurysm and polyp segmentation datasets demonstrate that ConTNet performs better due to other state-of-the-art methods.

Rupture of intracranial aneurysm is the first cause of subarachnoid hemorrhage, second only to cerebral thrombosis and hypertensive cerebral hemorrhage, and the mortality rate is very high. MRI technology plays an irreplaceable role in the early detection and diagnosis of intracranial aneurysms and supports evaluating the size and structure of aneurysms. The increase in many aneurysm images, may be a massive workload for the doctors, which is likely to produce a wrong diagnosis. Therefore, we proposed a simple and effective comprehensive residual attention network (CRANet) to improve the accuracy of aneurysm detection, using a residual network to extract the features of an aneurysm. Many experiments have shown that the proposed CRANet model could detect aneurysms effectively. In addition, on the test set, the accuracy and recall rates reached 97.81% and 94%, which significantly improved the detection rate of aneurysms.