Few-Shot Segmentation(FSS) aims to efficient segmentation of new objects with few labeled samples. However, its performance significantly degrades when domain discrepancies exist between training and deployment. Cross-Domain Few-Shot Segmentation(CD-FSS) is proposed to mitigate such performance degradation. Current CD-FSS methods primarily sought to develop segmentation models on a source domain capable of cross-domain generalization. However, driven by escalating concerns over data privacy and the imperative to minimize data transfer and training expenses, the development of source-free CD-FSS approaches has become essential. In this work, we propose a source-free CD-FSS method that leverages both textual and visual information to facilitate target domain task adaptation without requiring source domain data. Specifically, we first append Task-Specific Attention Adapters (TSAA) to the feature pyramid of a pretrained backbone, which adapt multi-level features extracted from the shared pre-trained backbone to the target task. Then, the parameters of the TSAA are trained through a Visual-Visual Embedding Alignment (VVEA) module and a Text-Visual Embedding Alignment (TVEA) module. The VVEA module utilizes global-local visual features to align image features across different views, while the TVEA module leverages textual priors from pre-aligned multi-modal features (e.g., from CLIP) to guide cross-modal adaptation. By combining the outputs of these modules through dense comparison operations and subsequent fusion via skip connections, our method produces refined prediction masks. Under both 1-shot and 5-shot settings, the proposed approach achieves average segmentation accuracy improvements of 2.18\% and 4.11\%, respectively, across four cross-domain datasets, significantly outperforming state-of-the-art CD-FSS methods. Code are available at https://github.com/ljm198134/TVGTANet.

As an important structure connecting the spine and lower limbs, the abnormal pelvis is one of the threats to human health worldwide, leading to millions of deaths every year. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially for pelvis fractures. Computer-aided pelvis segmentation (CPS) is a promising choice for the abnormal pelvis due to the great success of deep learning. However, when it comes to the abnormal pelvis diagnosis, the lack of training data has hampered the progress of CPS. To solve this problem, we have published a large-scale pelvis dataset, namely the Pelvis Computed tomography (CT) image (PCT14K) dataset. This dataset contains 14487 CT slices with the corresponding label for pelvis areas, while the existing largest public pelvis dataset part comes from existing data sets related to other organs with a lot of redundant information, and another part comes from the orthopedic hospital without a corresponding label. The proposed dataset enables the training of sophisticated segmentation networks for high-quality CPS. Some mainstream segmentation algorithms are trained and evaluated on the proposed PCT14K dataset and served as the baselines for future research. The published dataset will be available at https://github.com/YUAN-SIMING/PCT14K.

Appropriate acetabular cup placement is an important step for the prevention of postoperative dislocation in total hip arthroplasty. Existing state-of-the-art methods focus on developing statistical methods and image-matching techniques. Such developments employ regression equations or 3D planning software to evaluate the acetabular cup angles, and little attention has been given to automatic prediction with machine/deep learning. In addition, existing approaches ignore the explainability and clinical utility of models, which creates a tension between accuracy and explainability. This paper proposes an innovative hybrid framework for acetabular cup angle prediction. The proposed method is based on deep stacked network (DSN) and SHapley Additive exPlanations (SHAP) with four improvements: (1) an arc-support line segments-based unsupervised segmentation module, obtaining the region of interest (ROI) occupied by the acetabular cup component automatically and efficiently; (2) a multiscale feature extraction and attribute optimization module, obtaining comprehensive radiomic features including the conventional texture features and deep learning features; (3) appending a SHAP module to the framework to improve the explainability of models through three main steps: local explanation, global explanation, and interactive visualization; (4) our method employs DSN method to predict the acetabular cup angles, and it considers the decision curve analysis (DCA) to tackle the problem of clinical utility evaluation. By integrating the above improvements in series, the models’ performances are gradually enhanced. Experimental results show that our model achieves superior results to existing acetabular cup angle prediction approaches.

Spread spectrum communication is a typical scheme for covert communication because of its low detectability and antijam characteristic. However, the associated design concerns multiple factors, such as cochannel multiple access interference (MAI) and spread spectrum gain. In this paper, the lattice reduction theory is applied to MAI cancellation of spread spectrum communication and a novel lattice reduction aided multiple user detection method is proposed. The near maximum likelihood (ML) performance of MAI resistance is verified by simulation and theoretical analysis. The superiority of detection performance in strong MAI scenarios is especially addressed. Based on the algorithm, a collaborative covert communication system design is proposed. Low-power covert signals can be transmitted at a higher bit rate with the same coverage as more high-power cochannel signals. The covert transmission performance can be improved significantly compared to traditional designs.

Stream programming model has been productively applied to a number of important application domains. Software pipelining is an important code scheduling technique for stream programs. However, the multicore evolution has presented a new dimension of challenges: that is how to orchestrate the best software pipelining schedule in the face of resource constrained architectures (e.g., number of cores, available memory, and bandwidth)? In this paper, we proposed a new solution methodology to address the problem above. Our main contributions include the following. A unified Integer Linear Programming (ILP) formulation has been proposed that combines the requirement of both rate-optimal software pipelining and the minimization of intercore communication overhead. Next, an extended formulation has been proposed to formulate the schedule under memory size constrained systems. It orchestrates the rate-optimal software pipelining execution for stream programs with strict memory, processor cores, and communication constraints. A solution testbed has been implemented for the proposed problem formulations. This has been realized by extending the Brook programming environment with our software pipelining support-named DFBrook. An experimental study has been conducted to verify the effectiveness of the proposed solutions.

Stream programming model has been productively applied to a number of important applications domains. Software pipelining is an important code scheduling technique for stream programs. However, the multi-/many-core evolution has presented a new dimension of challenges: that is while searching a best software pipelining schedule how to ensure the communications between processing cores are also minimized? In this paper, we proposed a new solution methodology to address the above problem. Our main contributions include the following. A unified formulation has been proposed that combines the requirement of both rate-optimal software pipelining and the minimization of inter-core communication overhead. This formulation has been developed based on a synchronized dataflow graph model, and is expressed as an integer linear programming problem. A solution testbed has been implemented for the proposed problem formulation on the IBM Cell architecture. This has been realized by extending the Brook stream programming environment with our software pipelining support -- named DFBrook. An experimental study has been conducted to verify the effectiveness of the proposed solution. And a comparison of other scheduling methods has demon-strated the performance superiority of our proposed method.