Gate bootstrapping is a core primitive that enables arbitrary circuit evaluation in fully homomorphic encryption (FHE), where blind rotation remains the dominant performance bottleneck. In this work, we present a sub-millisecond NTRU-based gate bootstrapping scheme that achieves state-of-the-art performance through coordinated algorithmic, software, and hardware-level optimizations.

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.

TFHE is a fully homomorphic encryption scheme over the torus that supports fast bootstrapping. Its primary evaluation mechanism is based on gate bootstrapping and programmable bootstrapping (PBS), which computes functions while simultaneously refreshing noise. PBS-based evaluation is user-friendly and efficient for small circuits; however, the number of bootstrapping operations increases exponentially with the circuit depth. To address the challenge of efficiently evaluating large-scale circuits, Chillotti et al. introduced a leveled homomorphic evaluation (LHE) mode at Asiacrypt 2017. This mode decouples circuit evaluation from bootstrapping, resulting in a speedup of hundreds of times over PBS-based methods. However, the remaining circuit bootstrapping (CBS) becomes a performance bottleneck, even though its frequency is linear with the circuit depth.

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.

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.

The main task of medical image mining is to effectively analyze medical image data. Medical image classification algorithms have a high error rate near the threshold. To address the problem, the paper adopts a hybrid approach which combines computers algorithm and crowdsourcing system for image classification. A hybrid framework is proposed, which can achieve a higher accuracy significantly than only use classification algorithms. At the same time, it only processes the images that classification algorithms perform not well, so it has a lower monetary cost. In this framework, a range threshold is generated by using an efficient algorithm that assigns an image to a crowdsourcing or classification algorithm. To ensure the quality of crowdsourcing answers, this paper presents two worker models, Worker Quality Evaluation Model (WQEM)and Worker Performance Prediction Model(WPPM) respectively. Due to the lack of the crowdsourcing platform for processing medical information, medical image classification results are difficult to collect, so this paper proposed a crowdsourcing platform for medical image classification.