Hyperspectral imaging delivers high-resolution spectral-spatial information to support molecular tissue characterization, but its clinical utility is far from being fully realized. Existing segmentation techniques are constrained by fixed or suboptimal band selection strategies and insufficient frequency-domain modeling, which limit their ability to fully exploit discriminative spectral cues and subtle tissue structures. To address these challenges, we propose AMBS-SF2Net, a unified framework that enhances spectral representation and hierarchical frequency modeling for accurate and efficient segmentation. Specifically, the Adaptive Mask-based Band Selection (AMBS) module dynamically identifies informative spectral channels, the Adaptive Spectral-Frequency Integration (ASFI) module fuses multi-scale spatial edges and frequency-aware spectral features, and the Multi-Axis Frequency Enhanced (MAFE) module captures complementary spectral and spatial frequency patterns along different tensor dimensions. To rigorously evaluate the method’s generalizability, we conduct extensive experiments on datasets spanning distinct imaging scales, comprising a microscopic cholangiocarcinoma pathology dataset and two macroscopic tissue datasets of pig abdominal organs and human placenta. Results demonstrate that AMBS-SF2Net significantly outperforms state-of-the-art methods, exhibiting superior robustness across varying spectral resolutions and spatial modalities, thereby validating its strong potential for diverse clinical applications.

In recent years, deep learning has shown immense promise in advancing medical hyperspectral imaging diagnostics at the microscopic level. Despite this progress, most existing research models remain constrained to single-task or single-scene applications, lacking robust collaborative interpretation of microscopic hyperspectral features and spatial information, thereby failing to fully explore the clinical value of hyperspectral data. In this paper, we propose a microscopic hyperspectral universal feature perception framework (UFPF), which extracts high-quality spatial-spectral features of hyperspectral data, providing a robust feature foundation for downstream tasks. Specifically, this innovative framework captures different sequential spatial nearest-neighbor relationships through a hierarchical corner-to-center mamba structure. It incorporates the concept of "progressive focus towards the center", starting by emphasizing edge information and gradually refining attention from the edges towards the center. This approach effectively integrates richer spatial-spectral information, boosting the model's feature extraction capability. On this basis, a dual-path spatial-spectral joint perception module is developed to achieve the complementarity of spatial and spectral information and fully explore the potential patterns in the data. In addition, a Mamba-attention Mix-alignment is designed to enhance the optimized alignment of deep semantic features. The experimental results on multiple datasets have shown that this framework significantly improves classification and segmentation performance, supporting the clinical application of medical hyperspectral data. The code is available at: https://github.com/Qugeryolo/UFPF.

Hyperspectral imaging technology is considered a new paradigm for high-precision pathological image segmentation due to its ability to obtain spatial and spectral information of the detected object simultaneously. However, due to the time-consuming and laborious manual annotation, precise annotation of medical hyperspectral images is difficult to obtain. Therefore, there is an urgent need for a semi-supervised learning framework that can fully utilize unlabeled data for medical hyperspectral image segmentation. In this work, we propose an adversarial consistency constraint learning cross indication network (ACCL-CINet), which achieves accurate pathological image segmentation through adversarial consistency constraint learning training strategies. The ACCL-CINet comprises a contextual and structural encoder to form the spatial-spectral feature encoding part. The contextual and structural indications are aggregated into features through a cross indication attention module and finally decoded by a pixel decoder to generate prediction results. For the semi-supervised training strategy, a pixel perceptual consistency module encourages the two models to generate consistent and low-entropy predictions. Secondly, a pixel maximum neighborhood probability adversarial constraint strategy is designed, which produces high-quality pseudo labels for cross supervision training. The proposed ACCL-CINet has been rigorously evaluated on both public and private datasets, with experimental results demonstrating that it outperforms state-of-the-art semi-supervised methods. The code is available at: https://github.com/Qugeryolo/ACCL-CINet.

Nodes performing different functions in a network have different roles, and these roles can be gleaned from the structure of the network. Learning latent representations for the roles of nodes helps to understand the network and to transfer knowledge across networks. However, most existing structural embedding approaches suffer from high computation and space cost or rely on heuristic feature engineering. Here we propose RiWalk, a flexible paradigm for learning structural node representations as it decouples the structural embedding problem into a role identification procedure and a network embedding procedure. Through role identification, rooted kernels with structural dependencies kept are built to better integrate network embedding methods. To demonstrate the effectiveness of RiWalk, we develop two different role identification methods named RiWalk-SP and RiWalk-WL respectively and employ random walk based network embedding methods. Experiments on within-network classification tasks show that our proposed algorithms achieve comparable performance with other baselines while being an order of magnitude more efficient. Besides, we also conduct across-network role classification tasks. The results show potential of structural embeddings in transfer learning. RiWalk is also scalable, making it capable of capturing structural roles in massive networks.

In order to develop a nano UAV in a GPS-denied environment, a visual-inertial system (VINS) consisting of a monocular camera and a low-cost inertial measurement unit (IMU) should be integrated, as they make a minimum, ultra lightweight sensor suite that enables both autonomous flight and sufficient environment awareness. Although there are numerous potential applications, getting to understand the structure of the flight platform can be cumbersome and confusing. Packages and resources provided on the Qualcomm developer network (QCN) and their official GitHub group ATLFlight contain un-unified and disorderly chunks of information. In this paper, we present an organized documentation that presents in detail, the platform structure as a whole, from hardware to software architecture, including installation and API related applications.