Transformer-based segmentation methods exhibit considerable potential in medical image analysis. However, their improved performance often comes with increased computational complexity, limiting their application in resource-constrained medical settings. Prior methods follow two independent tracks: (i) accelerating existing networks via semantic-aware routing, and (ii) optimizing token adapter design to enhance network performance. Despite directness, they encounter unavoidable defects (e.g., inflexible acceleration techniques or non-discriminative processing) limiting further improvements of quality-complexity trade-off. To address these shortcomings, we integrate these schemes by proposing the semantic-aware adapter (SarAdapter), which employs a semantic-based routing strategy, leveraging neural operators (ViT and CNN) of varying complexities. Specifically, it merges semantically similar tokens volume into low-resolution regions while preserving semantically distinct tokens as high-resolution regions. Additionally, we introduce a Mixed-adapter unit, which adaptively selects convolutional operators of varying complexities to better model regions at different scales. We evaluate our method on four medical datasets from three modalities and show that it achieves a superior balance between accuracy, model size, and efficiency. Notably, our proposed method achieves state-of-the-art segmentation quality on the Synapse dataset while reducing the number of tokens by 65.6%, signifying a substantial improvement in the efficiency of ViTs for the segmentation task.

A major contributor to permanent vision loss is glaucoma. Early diagnosis is crucial for preventing vision loss due to glaucoma, making glaucoma screening essential. A more affordable method of glaucoma screening can be achieved by applying artificial intelligence to evaluate color fundus photographs (CFPs). We present the Justified Referral in AI Glaucoma Screening (JustRAIGS) challenge to further develop these AI algorithms for glaucoma screening and to assess their efficacy. To support this challenge, we have generated a distinctive big dataset containing more than 110,000 meticulously labeled CFPs obtained from approximately 60,000 patients and 500 distinct screening centers in the USA. Our objective is to assess the practicality of creating advanced and dependable AI systems that can take a CFP as input and produce the probability of referable glaucoma, as well as outputs for glaucoma justification by integrating both binary and multi-label classification tasks. This paper presents the evaluation of solutions provided by nine teams, recognizing the team with the highest level of performance. The highest achieved score of sensitivity at a specificity level of 95% was 85%, and the highest achieved score of Hamming losses average was 0.13. Additionally, we test the top three participants' algorithms on an external dataset to validate the performance and generalization of these models. The outcomes of this research can offer valuable insights into the development of intelligent systems for detecting glaucoma. Ultimately, findings can aid in the early detection and treatment of glaucoma patients, hence decreasing preventable vision impairment and blindness caused by glaucoma.

Foundation segmentation models like Segment Anything (SAM) exhibit strong generalization on natural images but struggle with localized failures in medical imaging, especially on fine-grained structures such as vessels with complex morphology and indistinct boundaries. To address this, we propose FineSAM++, a structure-aware sparse expert framework designed to refine SAM outputs by introducing a confidence-driven soft Routing Module. This module dynamically identifies structurally uncertain regions and activates a lightweight Residual Expert to model and correct residual structural errors only within these areas, thereby achieving efficient "refinement over retraining." Extensive experiments on five public vascular segmentation datasets demonstrate that FineSAM++ consistently outperforms both SAM-adapted baselines and task-specific models in terms of accuracy, topological consistency. Our results highlight the effectiveness of sparse, structure-driven Mixture-of-Experts (MoE) strategies for enhancing the reliability of foundation vision models in clinical image understanding tasks.

Autoencoder is an unsupervised neural network that learns effective representations of data and has wide applications in feature learning, data compression, etc. However, Autoencoder is very sensitive to noise, resulting in low generalization and robustness of the model. To solve this problem, we propose a stable and efficient Autoencoder model called nmFunc-Autoencoder. Inspired by the Neural Memory Ordinary Differential Equation, the Neural Memory Activation Function uses its excellent dynamic nonlinear implicit mapping to establish a mapping relationship between external inputs and stable values to ensure the stability of distinguishable feature extraction, thereby performing better robustness when subjected to noise attacks. We conduct robustness experiments to evaluate its performance. The result showed that compared with other Autoencoder models, the data features extracted by the proposed model are more robust. Subsequently, in the execution efficiency experiments and ablation study, the model was shown to be low-cost and effective.

With the development of science and technology, IoT devices have already become ubiquitous in the public eye. Through the perception layer, the collected data are displayed or transmitted to the server backend for analysis. Due to the increasing integration of IoT devices into people’s daily lives, privacy issues, such as data leaks, have received more attention. Most sensor nodes, such as temperature and pressure sensors in marine environments, have low computing power, storage capacity, and significant underlying heterogeneity, making it challenging to implement a standardized data security protection solution. Data security in the nodes is seriously challenged as a result. It is of great significance to design a lightweight block cipher for the Internet of Things (IoT) environment, which can ensure the security of node information. A new lightweight block cipher algorithm called ALICA is proposed in this paper, which is well‐suited for the low computing power and heterogeneous device environment at the lower layers. A generalized Feistel structure and a linear structure with XOR and shift operations are used to achieve ease of software implementation. Two different S‐boxes are used to create the nonlinear structure of the password, thereby enhancing the robust security of the cipher. In addition, the cipher adopts a design approach that prioritizes software efficiency while also considering hardware implementation efficiency. This makes it more suitable for low computing power, storage capacity, and resource‐limited IoT sensor nodes at the lower layers.