Edge computing (EC), as a computing paradigm that mitigates cloud load and reduces task latency, has attracted widespread attention from both academia and industry. Current research on EC primarily focuses on edge task offloading problems, while effectively matching tasks with microservices after offloading is also crucial for reliable task processing. Therefore, considering the differentiated hardware resource requirements of various task types, this paper designs a heterogeneous computing model that enables precise matching between tasks and edge server (ES) computational capabilities. To ensure ESs proactively deploy effective microservices and maintain trustworthy operations, we introduce an incentive mechanism and an ES discriminator algorithm. Considering diverse demands in EC scenarios, where ESs pursue higher incentive returns while reducing energy consumption, and the system aims to minimize latency and maintain reliability under limited incentive budgets, we construct an interconnected satisfaction model between ESs and the system. Based on this, we propose a bilateral consensus service placement (BCSP) algorithm that balances incentive consensus between ESs and the system, achieving rational microservice deployment and optimized task processing efficiency. Experimental results show that compared with existing algorithms, the proposed BCSP algorithm better accommodates multi-party requirements and enhances both efficiency and reliability in microservice placement.

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.

Electrocardiogram (ECG) signals play a critical role in the clinical diagnosis of arrhythmias. However, their non-stationary nature, multi-lead configuration, and multi-label annotations pose significant challenges for feature extraction and discriminative modeling. Existing approaches often rely on singlescale features or neglect the semantic dependencies among labels, limiting their ability to capture the intricate time-frequency patterns and label correlations inherent in ECG data. To address these issues, we propose a novel multi-label ECG classification framework, MTA-Net, which enhances the modeling of complex pathological features from both time-frequency and semantic perspectives. Specifically, MTA-Net leverages the Discrete Wavelet Packet Transform to extract high-resolution, multi-scale timefrequency representations, significantly improving the sensitivity to localized rhythm abnormalities. A label embedding module is introduced to incorporate label information into the feature interaction process, establishing explicit semantic connections between the samples and labels. Furthermore, a dual-branch masked encoder is designed to separately model structural and semantic representations of ECG signals. Finally, a labelguided decoder integrates these structural and semantic cues to produce accurate multi-label predictions. Extensive experiments conducted on two public multi-label ECG datasets, PTB-XL and CPSC2018, demonstrate that MTA-Net outperforms existing state-of-the-art methods in terms of AUC, F1-score, and accuracy, validating its effectiveness in handling complex multi-label ECG classification tasks.

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.

As smart cities evolve, rising computational demands strain infrastructures. Offloading tasks to edge cloud data centers offers potential but faces challenges like high latency, energy use, and data leakage, especially in dense urban areas. This paper presents a low-latency, energy-efficient digital twin (DT) architecture tailored for smart cities, integrating edge computing (EC) and multiple s (IRS) to enhance communication. Dynamic voltage and frequency scaling (DVFS) technology is considered for user devices to reduce energy consumption. To mitigate the risk of user privacy leakage during task offloading, we address sensitive user location data that may be exposed by proposing a perturbed sliding task queue (PSTQ) algorithm based on differential privacy (DP), and demonstrate the effectiveness of the algorithm. To optimize task processing time and energy efficiency, we decompose the complex problem using block coordinate descent and propose an intelligent scheduling for energy sustainability (ISES) algorithm based on Karush-Kuhn-Tucker conditions and deep reinforcement learning (DRL). Experimental results demonstrate that our proposed architecture and algorithms achieve over 90% improvement in key optimization objectives, alleviating the computational pressure on existing devices while significantly enhancing task processing efficiency and energy sustainability.

In the rapidly advancing landscape of intelligent medical technology, high-precision and widely applicable medical image segmentation techniques are pivotal in advancing personalized treatment plans and enhancing patient experiences. To address inherent obstacles, such as complex structures and features that are difficult to capture in segmentation tasks, we first propose a CMH module that merges convolutional neural networks (CNNs) with state space models (e.g., Mamba). This module hierarchically integrates CNN’s local priors into the Mamba layer, adeptly capturing specific details of target objects and broader contextual information from different perspectives. Subsequently, we introduce an information fusion module based on Mamba (MIH), aimed at dynamically fusing feature information from different paths and levels to enhance feature expression and discrimination. Building upon the abovementioned modules, we develop an unexplored segmentation model, CMHNet, capable of flexibly capturing feature maps of varying scales and angles from medical images. In the experiments, we present CMHNet’s distinguished performance in segmentation tasks through massive experiments on four medical datasets.

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

MicroRNAs (miRNAs) have significant implications in diverse human diseases and have proven to be effectively targeted by small molecules (SMs) for therapeutic interventions. However, current SM-miRNA association prediction models do not adequately capture SM/miRNA similarity. Matrix completion is an effective method for association prediction, but existing models use nuclear norm instead of rank function, which has some drawbacks. Therefore, we proposed a new approach for predicting SM-miRNA associations by utilizing the truncated schatten p-norm (TSPN). First, the SM/miRNA similarity was preprocessed by incorporating the Gaussian interaction profile kernel similarity method. This identified more SM/miRNA similarities and significantly improved the SM-miRNA prediction accuracy. Next, we constructed a heterogeneous SM-miRNA network by combining biological information from three matrices and represented the network with its adjacency matrix. Finally, we constructed the prediction model by minimizing the truncated schatten p-norm of this adjacency matrix and we developed an efficient iterative algorithmic framework to solve the model. In this framework, we also used a weighted singular value shrinkage algorithm to avoid the problem of excessive singular value shrinkage. The truncated schatten p-norm approximates the rank function more closely than the nuclear norm, so the predictions are more accurate. We performed four different cross-validation experiments on two separate datasets, and TSPN outperformed various most advanced methods. In addition, public literature confirms a large number of predictive associations of TSPN in four case studies. Therefore, TSPN is a reliable model for SM-miRNA association prediction.