Federated semi-supervised learning (FSSL) enables collaborative training on distributed medical data while preserving privacy, but faces challenges from data heterogeneity and class imbalance. These issues degrade pseudo-labeling quality and introduce confirmation bias. To overcome these limitations, this paper proposes FedPPL, a novel framework for federated medical image classification. FedPPL comprises two key components: Prototype-Aware Thresholding (PAT), which adaptively adjusts pseudo-labeling thresholds using global to mitigate confirmation bias, and Prototype Contrastive Learning (PCL), which enhances feature discriminability to boost accuracy. Experiments on FedISIC2019 and MedMNIST demonstrate that FedPPL achieves more robust and balanced performance than state-of-the-art methods, proving its potential for building reliable and privacy-preserving diagnostic models.

Automatic medical report generation (MRG) enhances clinical efficiency but still remains challenging due to cross-modal misalignment between lesions and text, exacerbated by data bias from scarce abnormalities and excessive normal cases diluting focus on critical lesions. To rectify these deficiencies, this paper proposes a new method called Multi-level Contrastive Learning with Feature Enhancement Network (MCL-FENet). Visual Feature Enhancement Module (VFEM) enhances channel-wise discriminability to improve lesion localization. Cross-Modal Feature Enhancement Module (CMFEM) integrates historical report semantics with current images via multimodal fusion, emulating radiologists' diagnostic reasoning. Furthermore, Global Contrastive Learning Module (GCLM) and Local Contrastive Learning Module (LCLM) jointly improve cross-modal alignment at both semantic and fine-grained levels. Experiments on the IU X-ray dataset demonstrate that MCL-FENet outperforms existing state-of-the-art methods.

In medical image segmentation tasks, diffusion models have exhibited significant potential. However, mainstream diffusion models show drawbacks including multiple sampling times and slow prediction results. Recently, as a standalone generative network, consistency models have resolved the existing issue. Compared to diffusion models, consistency models can lower the sampling times to once, not only achieving similar generative effects but also significantly accelerating training and prediction. However, they are not suitable for image segmentation tasks. Meanwhile, their application in the medical imaging field has not yet been investigated. Therefore, this study employs the consistency model to perform medical image segmentation tasks, designing multi-scale feature signal supervision modes and loss function guidance to realize model convergence. Experiments have demonstrated that the CTS model is capable of obtaining better medical image segmentation results with a single sampling during the test phase.

Medical Visual Question Answering (Med-VQA) aims to accurately answer clinical questions related to medical images. Due to the challenges in collecting medical images and the limited scale of datasets, extracting image features has become highly challenging. Traditional Med-VQA tasks are often treated as multi-class classification problems, overlooking the relationships between candidate answers. In this paper, a novel answer selection-based loss function is proposed to quantify the differences between original answers, addressing the aforementioned issues. For the answer with a higher probability among the selected answers, the similarity between the predicted answer and the true answer is calculated, and the loss is derived. Additionally, a representation enhancement module is introduced that focuses more on regions receiving common attention from various feature extractors. Experimental results demonstrate that the proposed method exhibits strong performance on multiple Med-VQA datasets and effectively improves the accuracy of existing Med-VQA models. Furthermore, the method can be seamlessly integrated into existing Med-VQA models to enhance their accuracy.

Federated learning enables collaborative learning across distributed medical institutions without centralizing data. However, existing studies often overlook class imbalance in medical images, which can degrade model performance, especially for minority classes. In this paper, we propose FedCRGP, a method that uses Gaussian-distributed prototypes to address class imbalance in federated medical image classification. These prototypes incorporate class variance to enhance representation learning. Class-aware Gaussian Prototype Learning (CGPL) is introduced to improve intra-class similarity and reduce interclass similarity by aligning class features around the Gaussian-distributed prototypes. Local and global features sampled from these prototypes are fused to retrain the classifier, mitigating bias caused by class imbalance. Experiments on two medical image classification datasets demonstrate its superior performance, particularly in multi-class scenarios.

Medical images are complex, and the annotation of medical images requires high expertise. It would be time-consuming and costly to annotate directly for experts. As a result, one of the primary challenges currently faced by medical visual question answering (VQA) is the lack of large-scale annotated data. To address this issue, a Weak Supervision-based Robust Pretraining (WSRP) method for medical VQA is proposed. Specifically, our method builds upon a contrastive language-image pretraining framework by introducing adversarial training. However, the contrastive language-image pretraining framework, by treating each image-text pair as a separate category, may lead to class collision problems, thereby affecting the quality of image representation. Therefore, weakly supervised contrastive learning is introduced to generate weak labels, enabling the model to learn fine-grained feature representations. The proposed weak supervision-based robust pretraining method for medical VQA is empirically evaluated and experimental results on public datasets demonstrate its superior performance.

Sequential recommendations are designed to capture user preferences based on their past actions and predict the items they may interact with in the next moment. Benefiting from the self-attention mechanism, methods that utilize side information (such as item categories or brand) to improve the prediction performance of sequential recommendation have yielded promising results. Previous approaches typically directly fuses side information embeddings into item embeddings as inputs to the model. However, this fusion approach overlooks the distinctions in various types of information in sequential pattern inference, and also failing to fully model the relationship between items and side information. In this work, we propose a Dual-Path Side Information Fusion method (DPIF) to better utilize side information for improved recommendation performance. Our model employs two parallel paths for side information fusion modeling. One path obtains the relationship representation within the items and the side information, and the other path obtains the relationship representation between the items and the side information. Subsequently, an attention-based adaptive fusion module is utilized to combine inter-attribute relationship and intra-attribute relationship representation, generating the final user preferences. Extensive experiments were conducted on four real-world datasets, demonstrating the effectiveness of the introduced model. Our source code is available at https://github.com/ZhangYu-x/DPIF.

Abstract Skin cancer is one of the deadliest cancers, and it has been widely developed worldwide since the last decade. Malignant melanoma is currently the most deadly skin cancer. If malignant melanoma is diagnosed at an early stage, the probability of patients being cured will be greatly improved. At present, most existing skin lesion image classification methods only use deep learning. However, the multi‐modal features of skin lesions in the medical domain are not well utilized and integrated. To reduce the classification error of the skin lesion images caused by the complexity and subjectivity of visual interpretation, a malignant melanoma dermoscopy image classification method based on multi‐modal medical features is proposed in this paper which is inspired by the fuzzy decision‐making process of doctors. It can reduce the subjective difference in the image classification process and assist dermatologists to analyze the skin lesion area. Firstly, the feature detection method based on the extension theory can effectively quantify the difference between different colour features. Then, an interpretable segmentation edge of the skin lesion is established by using the neutrosophic theory which can convert the image into the neutrosophic space. The edge of the skin lesion is captured by applying the Hierarchical Gaussian Mixture Model (HGMM) method. Next, the edge sequence is established by segmenting the edge, and the contour regularity, symmetry, and uniformity of the edge of the skin lesion are analyzed. Finally, the extracted multi‐feature sets are used for dermoscopy image classification. Experiments are carried out on real datasets, and the classification accuracy of four kernel functions is verified. The experimental results show that the authors’ method can effectively improve the classification accuracy of benign dermoscopy images and malignant dermoscopy images.

Interpreting ocean remote sensing images is still a challenge that is worth studying because they can carry valuable information for various important applications. Due to the absence of labeled datasets, unsupervised object-based image analysis (OBIA) methods provide an effective solution to understand remote sensing images with the advantage of grouping local similar pixels into a homogeneous area. However, ocean remote sensing images usually have the characteristics of large size, large background, and coexisting of large and small objects, which results in previous OBIA methods easily falling into the difficulty of accurately segmenting the large and small objects at the same time and the dilemma of time-consuming computation. To solve this problem, a novel multiscale and multisubgraph (MSMSG)-based image segmentation method is presented in this article. First, a coarse-to-fine superpixel generation method is designed to generate optimal superpixels, which can not only solve the problem of coexisting large objects and small objects but also the problem of manually setting the initial segmentation number. Second, the proposed background removal strategy helps to eliminate the trouble of large background areas in ocean remote sensing images. Third, a multisubgraph is constructed with the help of background removal. Finally, the MSMSG merging strategy is addressed to group all similar superpixels into the same cluster, which not only reduces the useless computation of nonadjacent superpixels but also avoids segmentation errors with the same scale. Experiments conducted on three different datasets show that the proposed segmentation method is high-performance and high-efficiency.

With the continuous improvement of people’s quality of life, air quality issues have become one of the topics of daily concern. How to achieve accurate predictions of air quality in a variety of complex situations is the key to the rapid response of local governments. This paper studies two problems: (1) how to predict the air quality of any monitoring station based on the existing weather and environmental data while considering the spatiotemporal correlation among monitoring stations and (2) how to maintain the accuracy and stability of the forecast even when the available data is severely insufficient. A prediction model combining Long Short‐Term Memory networks (LSTM) and Graph Attention (GAT) mechanism is proposed to solve the first problems. A metalearning algorithm for the prediction model is proposed to solve the second problem. LSTM is used to characterize the temporal correlation of historical data and GAT is used to characterize the spatial correlation among all the monitoring stations in the target city. In the case of insufficient training data, the proposed metalearning algorithm can be used to transfer knowledge from other cities with abundant training data. Through testing on public data sets, the proposed model has obvious advantages in accuracy compared with baseline models. Combining with the metalearning algorithm, it gives a much better performance in the case of insufficient training data.

Location-based services have become a mainstream in people’s daily lives due to continuous innovations in the field of mobile networking and GPS technologies. Recently they have advanced into a hot topic to which the majority of researchers pay close attention about how to enjoy them while safeguarding the location privacy of mobile users. Existing works involve the injection of random noise that cannot pledge the quality of service. Herein this manuscript, we propose a novel location privacy protection model based on the loss of service quality. This model allows the user to express his/her requirement of service quality by specifying the maximum service quality loss Lmax , which is the user’s tolerance. Lmax can be set to 0. Our comprehensive experimental evaluation using a real-world dataset demonstrates that our modus outdoes other state-of-the-art approaches.

A number of security and privacy challenges of cyber system are arising due to the rapidly evolving scale and complexity of modern system and networks. The cyber system is a fundamental ingredient for Internet of Things (IoT) and smart city which are driven by huge amount of data. These data carry a lot of information for mining and analysis, especially trajectory data. If unprotected trajectory data is released, it may disclose user’s personal privacy, such as home, religion, and behavior mode, which will endanger their personal security. Until now, many methods for protecting trajectory information have been proposed. However, these methods have the following deficiencies: (i) they cannot defend against speculative attacks if the attacker’s background knowledge is maximized; (ii) when studying the problem, they made some strong assumptions that did not match the reality; (iii) the implementation algorithm is complicated and the time complexity is high, which means that data cannot be executed quickly when the amount is large. So, in this paper, we propose a spatial partition based method to publish trajectory data via differential privacy. First, by exponential mechanism, we divide location set at the same time into different partitions fast and accurately. Then we propose another effective method to release trajectory in a differential private manner. We design experiment based on the real-life dataset and compare it with existing method. The results show that the trajectory dataset released by our algorithm has better usability while ensuring privacy.