While modern segmentation models often prioritize performance over practicality, we advocate for a design philosophy that prioritizes simplicity and efficiency, and strive to design high-performance segmentation models. This paper presents SimpleUNet, a scalable, lightweight medical image segmentation framework. The key is that we proposed a simple yet effective partial feature selection mechanism for reducing information redundancy and thus facilitating compact model design. Additionally, we found that adjusting the model width is a straightforward yet easily overlooked tactic for lightweight model design, thereby preventing exponential parameter growth across network stages. By integrating an almost parameter-free channel attention module, the performance of the developed models can be improved with minimal overhead. Leveraging these techniques, our record-breaking model SimpleUNet with only 16 KB parameters surpasses LBUNet and other lightweight benchmarks across multiple public datasets. Impressively, the 0.67 MB variant achieves superior efficiency and accuracy, attaining a mean DSC/IoU of 85.76%/75.60% on a curated multi-center breast lesion dataset, surpassing both U-Net and TransUNet. Evaluations on skin lesion datasets (ISIC 2017/2018: mDice 84.86%/88.77%) and endoscopic polyp segmentation (KVASIR-SEG: 86.46%/76.48% mDice/mIoU) confirm consistent dominance over state-of-the-art models. Although our current SimpleUNet architecture does not rely on exotic or custom operators, it is fundamentally designed to embrace future innovations. The framework remains fully compatible with emerging operator-level advancements, allowing effortless integration and seamless upgrades without structural modifications. Codes can be found at https://github.com/Frankyu5666666/SimpleUNet .

The encapsulated route images provided by vendors hide detailed implementation codes of routing protocol, making it challenging to implement the same routing protocol on heterogeneous devices, as well as to locate and repair the configuration errors. While some works attempt to address the above issues, they fail in large-scale deployment, manifested in insufficiency in complex routing behaviors, lack of clear mapping between routing behavior and protocol standards, and difficulty in obtaining an open-source license. To solve all the problems, we propose RTB, a novel tool to mine the causality between network configurations and routing behaviors, in order to assist operations personnel in better pinpointing configuration errors. First, RTB generates test cases based on configuration features using a coverage-guided combinatorial testing algorithm. Then, RTB employs a differential network model to obtain differential datasets storing the correspondence between configurations and routing behaviors. Finally, RTB utilizes the differential datasets to create multilayer causal graphs, so as to reason about the implementation details and differences in routing protocols, which assists network operation and maintenance personnel in locating configuration errors. Experimental results show that RTB only needs 35.3 % test cases, and improves code coverage by 10 % over Metha. Moreover, RTB can mine the causality with 98 % accuracy, which is 5 % more efficient than existing tools.

Network device misconfigurations can lead to security vulnerabilities and operational failures. Existing validation methods primarily rely on program analysis or machine learning, which are often labor-intensive and data-sensitive. While Large Language Models (LLMs) excel in text analysis, their direct application to configuration validation faces challenges such as limited domain knowledge, hallucinations, and non-interpretability. To address these issues, we propose ConfigChecker, an automated network configuration validation system that integrates LLMassisted structured knowledge extraction with knowledge graphbased reasoning. ConfigChecker involves two key phases: (1) Knowledge Graph Construction - We construct three instructiontuning datasets (EV, PG, and RE) to fine-tune LLMs for structured command information extraction, including view transitions, parameter constraints, and inter-command dependencies. The extracted knowledge is stored in a knowledge graph, providing a foundation for configuration validation. (2) Configuration Validation - A graph-based command validator ensures syntax and semantic correctness, while knowledge graph reasoning and indexing mechanism are used to detect inter-command dependency violations. We evaluate six instruction-tuned LLMs for structured knowledge extraction, achieving up to 90% accuracy on the EV and RE datasets. Additionally, our system shows a 9%-20% improvement in validation accuracy compared to three mainstream large models. Furthermore, the average response time per configuration line remains below 15 milliseconds. Results demonstrate that ConfigChecker significantly enhances validation accuracy and efficiency, providing a novel integration of LLMs and knowledge graphs for automated configuration validation.

Segment Routing over IPv6 (SRv6) is an innovative and adaptable source routing technique that enhances interconnection services. It plays a pivotal role in next-generation networking technologies, providing crucial support for network telemetry, computing power networks, and related technologies. The end-to-end connectivity capability of SRv6 is highly regarded by ISPs, driving its widespread deployment in networks. However, configuring an SRv6 network can be challenging and prone to errors due to the complexity of low-level configuration languages and numerous protocol parameters. To address this issue, we present AutoSRv6, a system designed to synthesize SRv6 configurations for large, evolving networks using high-level abstractions of network topology and policies. AutoSRv6 leverages formal constraint-solving techniques and SMT solvers to compute protocol parameters and generate configuration files that align with network policies. Furthermore, AutoSRv6 incorporates a mechanism to overcome the constraints imposed by hardware, mapping the end-to-end path to a SID (Segment Identifier) sequence. We have developed a prototype of AutoSRv6 and conducted experiments on diverse network topologies, evaluating its performance with various network policies. The results show that autoSRv6 can generate the network configuration satisfying the policy, the time cost of IGP synthesis is better than the existing method, and the length of the segment list is optimized by more than 2 times.

Fetal multi-anatomical structure detection in ultrasound (US) images can clearly present the relationship and influence between anatomical structures, providing more comprehensive information about fetal organ structures and assisting sonographers in making more accurate diagnoses, widely used in structure evaluation. Recently, deep learning methods have shown superior performance in detecting various anatomical structures in ultrasound images, but still have the potential for performance improvement in categories where it is difficult to obtain samples, such as rare diseases. Few-shot learning has attracted a lot of attention in medical image analysis due to its ability to solve the problem of data scarcity. However, existing few-shot learning research in medical image analysis focuses on classification and segmentation, and the research on object detection has been neglected. In this paper, we propose a novel fetal anatomical structure few-shot detection method in ultrasound images, TKR-FSOD, which learns topological knowledge through a Topological Knowledge Reasoning Module to help the model reason about and detect anatomical structures. Furthermore, we propose a Discriminate Ability Enhanced Feature Learning Module that extracts abundant discriminative features to enhance the model's discriminative ability. Experimental results demonstrate that our method outperforms the state-of-the-art baseline methods, exceeding the second-best method with a maximum margin of 4.8% on 5-shot of split 1 under four-chamber cardiac view.

Salient object detection in optical remote sensing images (RSI-SOD) aims to segment objects that attract human attention in optical RSIs. With the tremendous success of full convolutional neural networks (FCNs) for pixel-level segmentation, the performance of RSI-SOD has improved significantly. However, most RSI-SOD methods primarily focus on enhancing detection accuracy, neglecting memory and computational costs, which hinders their deployment in resource-constrained applications. In this paper, we propose a novel lightweight RSI-SOD network, named MEANet, to address these challenges. Specifically, a multiscale edge-embedded attention (MEA) module is designed to enhance the capture of salient objects by incorporating edge information into spatial attention maps. Building upon this module, a U-shaped decoder network is constructed, and a multilevel semantic guidance (MSG) module is introduced to mitigate the issue of semantic dilution in U-shaped networks. Through extensive quantitative and qualitative comparisons with 27 state-of-the-art FCN-based models, the proposed model demonstrates competitive or superior performance, while maintaining only 3.27M parameters and 9.62G FLOPs. The code and results of our method are available at https://github.com/LiangBoCheng/MEANet .

Congenital heart disease (CHD) is the most common congenital disability affecting healthy development and growth, even resulting in pregnancy termination or fetal death. Recently, deep learning techniques have made remarkable progress to assist in diagnosing CHD. One very popular method is directly classifying fetal ultrasound images, recognized as abnormal and normal, which tends to focus more on global features and neglects semantic knowledge of anatomical structures. The other approach is segmentation-based diagnosis, which requires a large number of pixel-level annotation masks for training. However, the detailed pixel-level segmentation annotation is costly or even unavailable. Based on the above analysis, we propose SKGC, a universal framework to identify normal or abnormal four-chamber heart (4CH) images, guided by a few annotation masks, while improving accuracy remarkably. SKGC consists of a semantic-level knowledge extraction module (SKEM), a multi-knowledge fusion module (MFM), and a classification module (CM). SKEM is responsible for obtaining high-level semantic knowledge, serving as an abstract representation of the anatomical structures that obstetricians focus on. MFM is a lightweight but efficient module that fuses semantic-level knowledge with the original specific knowledge in ultrasound images. CM classifies the fused knowledge and can be replaced by any advanced classifier. Moreover, we design a new loss function that enhances the constraint between the foreground and background predictions, improving the quality of the semantic-level knowledge. Experimental results on the collected real-world NA-4CH and the publicly FEST datasets show that SKGC achieves impressive performance with the best accuracy of 99.68% and 95.40%, respectively. Notably, the accuracy improves from 74.68% to 88.14% using only 10 labeled masks.