Accurately predicting isocitrate dehydrogenase (IDH) mutations is crucial for glioma diagnosis, but the limited availability of multimodal MRI restricts the generalization of existing methods. Fine-tuning foundation models is a common solution, yet their lack of domain-specific knowledge often impairs optimal performance. Clinical studies show that both the imagemodal T2-FLAIR mismatch sign knowledge and text-modal demographic information are useful for IDH genotyping. Thus, we propose a novel network that integrates multimodal clinical knowledge to guide the fine-tuning of the multimodal foundation model M3D for glioma IDH genotyping on multimodal MRI. To fully utilize multimodal knowledge, we first extract multigranularity T2-FLAIR mismatch features from different layers via a Mixture-of-Experts (MoE) pool-based mismatch adapter (incorporating an MoE-based pooling mechanism and a spatialchannel mismatch attention module). Meanwhile, demographic information is converted into text prompts and encoded by the M3D text encoder to generate demographic-based text embeddings. In the encoder, T2-FLAIR mismatch features are integrated at the end of each ViT Block to introduce imagingspecific knowledge, while text features are further processed via a cross-modal text-image attention fusion adapter to enhance representation learning in the joint feature space. We evaluated the approach on an internal dataset (from 3 public datasets, 871 patients) and an independent external dataset (501 patients). It achieved 93.25 % accuracy on the internal dataset and 86.03 % on the external dataset, with only$\mathbf{2. 6 7 M}$trainable parameters, outperforming 7 existing state-of-the-art IDH genotyping methods.

Deformable image registration is fundamental in medical image analysis. Existing pyramid-based deep learning methods suffer from coarse deformation decomposition, poor inter-level transitions, and error accumulation. To address these, we propose GMReg, an unsupervised pyramid network for medical image registration based on Mamba correlation. GMReg introduces intra-level multi-scale decomposition: each pyramid level splits deformation fields into subfields with different receptive fields via grouping, using Group Mamba correlation layers for feature matching/fusion, and convolutional prediction for sub-fields. A channel attention-based context fusion module enhances inter-group interaction, while a multi-scale edge enhancement module guides subfield fusion to improve boundary sensitivity. Experiments on two public brain MRI datasets (LPBA40, Mind-Boggle) show GMReg significantly outperforms state-of-the-art methods in registration accuracy. Additionally, results on the FIRE dataset demonstrate that GMReg also holds potential for the fundus image registration task.

The Alberta Stroke Program Early CT Score (AS-PECTS) is a systematic method for assessing the extent of early ischemic changes on non-contrast CT (NCCT) of patients with acute ischemic stroke (AIS). The ASPECTS regions are anatomically and physiologically interconnected, making them suitable for analysis by graph neural networks. However, most existing methods fail to effectively use the relationships and bilateral differences. This study designs an Anatomical Relationship and Isomorphic Graph approximation guided Graph Convolutional Network for ASPECTS scoring on NCCT. Firstly, we propose a node construction guided by anatomical structures, i.e., utilizing the region anatomical relationship adjacency matrix of the ASPECTS regions to build the nodes. Secondly, to optimize the information propagation among nodes, we propose isomorphic graph approximation, utilizing edge learning, connectivity-based subgraph selection, and supervised isomorphic subgraph approximation to supervise the construction of isomorphic subgraphs. We validate our method on private AIS datasets which included NCCT scans of 257 AIS patients. The results show that the proposed method achieves interclass correlation coefficients of 0.8554 for total ASPECTS, and accuracy of 90.61% for dichotomized ASPECTS scoring (4), outperforming 9 state-of-the-art methods.

Segmentation of lesions in Acute Ischemic Stroke (AIS) patients on Non-Contrast CT (NCCT) scans is pivotal for expedited diagnosis and effective treatment planning. The subtle and 4.5-hour golden treatment window characteristic of AIS lesions on NCCT makes fully automated segmentation more preferred. We introduce AISCT-SAM, a fine-tuned Segment Anything Model (SAM), for this task. Specifically, we: 1) integrate an adaptive gate control mechanism with Adapter and Low-Rank Adaptation to comprehensively fine-tune the encoder of the foundational SAM model; 2) incorporate a depth adapter to extract richer 3D contextual information, enhancing lesion continuity; 3) propose a self-prompt generator that leverages bilateral hemisphere differences to generate prompts for the mask decoder, enabling fully automatic segmentation. It is evaluated on both public and private datasets, with results from internal and external validations showing state-of-the-art performance. Additionally, volumetric analysis indicates that AISCT-SAM can provide valuable reference for diagnostic and treatment decisions. Our data and code are publicly available at: https://github.com/GitHub-TXZ/AISCT-SAM.

Nowadays, Congestion Control (CC) algorithms are expected to satisfy the diverse demands of applications running over diverse networks. To achieve this goal, the combinations, which are expected to inherit both the advantages of classic CC in terms of convergence, overhead, and explainability, and the advantages of learning-based CC on adapting to diverse networks and demands, become a hot topic. In this paper, we reveal the existing combination works are eithergiving a fishorteaching to fish. Based on the insight of their essential issues, we develop the Concentrator method ofteaching to fish. According to this method, we propose Seagull as a step further. Specifically, Seagull captures the network characteristics and application demands in a coarse-grained manner via an online learning module. Moreover, the online learning module guides the customization of the rate adjustment rules of the classic CC module for fine-grained system evolution. Replacing the assumption on networks by the captured characteristics, the classic CC module of Seagull can fulfill the specified application demands. Real-world experimental results show Seagull respectively outperforms Orca, PCC-Vivace, and CUBIC by$49.3\%,\ 30.4\%$, and 24.9% in terms of throughput over the Internet, and improves the video quality of experience (QoE) by$12.9\sim 33.5\%$compared to CUBIC over cellular links.

Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) has been widely used in Mild Cognitive Impairment (MCI) detection. However, most existing methods rely on fixed sliding windows to capture the temporal variations in brain connectivity and fail to model temporal features effectively. To address these limitations, we propose a spatio-temporal graph Transformer framework. First, we segment rs-fMRI data into distinct segments using the multivariate Gaussian distribution of brain region signals and remove redundant segments based on cumulative Jensen-Shannon divergence. Next, we extract features for each brain region and time point within segments, using these temporal features to guide spatial feature aggregation. A position encoder is designed to leverage temporal features across segments to capture both local and global spatio-temporal information. Finally, we employ a population graph framework as the classifier, with spatio-temporal features and demographic data of each subject forming the edge weights, to output the detection results. Our method achieves 92.80%, 81.89%, and 82.80% accuracy in MCI detection, early MCI detection, and late MCI detection tasks on the ADNI3 and ADNI2 datasets, respectively, outperforming existing methods.

Lesion segmentation is a fundamental step for the diagnosis of acute ischemic stroke (AIS). Non-contrast CT (NCCT) is still a mainstream imaging modality for AIS lesion measurement. However, AIS lesion segmentation on NCCT is challenging due to low contrast, noise and artifacts. To achieve accurate AIS lesion segmentation on NCCT, this study proposes a hybrid convolutional neural network (CNN) and Transformer network with circular feature interaction and bilateral difference learning. It consists of parallel CNN and Transformer encoders, a circular feature interaction module, and a shared CNN decoder with a bilateral difference learning module. A new Transformer block is particularly designed to solve the weak inductive bias problem of the traditional Transformer. To effectively combine features from CNN and Transformer encoders, we first design a multi-level feature aggregation module to combine multi-scale features in each encoder and then propose a novel feature interaction module containing circular CNN-to-Transformer and Transformer-to-CNN interaction blocks. Besides, a bilateral difference learning module is proposed at the bottom level of the decoder to learn the different information between the ischemic and contralateral sides of the brain. The proposed method is evaluated on three AIS datasets: the public AISD, a private dataset and an external dataset. Experimental results show that the proposed method achieves Dices of 61.39% and 46.74% on the AISD and the private dataset, respectively, outperforming 17 state-of-the-art segmentation methods. Besides, volumetric analysis on segmented lesions and external validation results imply that the proposed method is potential to provide support information for AIS diagnosis.

With the volume and variety of network attacks increasing, efficient approaches to detect and stop network attacks before they damage the system or steal data is paramount to users and network administrators. Although many different detection mechanisms have been proposed, exiting detection methods generally tend to successfully detect attacks only after the attacks have finished and caused damage to the system. As recent attacks employ polymorphism technology and complicated attack techniques, it has become even more difficult for these approaches to detect attacks in a timely manner. In this paper, we propose an efficient network attack detection algorithm called seed expanding (SE) that detects attacks before they damage the system. SE employs the Two-Seed-Expanding network traffic clustering scheme, which clusters attack traffic into different attack phases. First we pre-process the networks traffic, including constructing the network flow, changing continuous-valued attributes into nominal attributes by adopting the discretization method, and further turning into binary features. Then based on these features, SE computes a weight for each flow and iteratively selects seeds to expand until all flows are divided into clusters. To investigate the effectiveness of the proposed approach, we undertook extensive experimental analyses. The results of the experiment show that the pre-procession greatly improves clustering performance, and the Two-Seed-Expanding Algorithm is better than K-Means and other kinds of Seed-Expanding in attack-flow clustering. These cluster results can be further used in attack detection.

In recent years, worm signatures suffer from difficulties to detect polymorphic worms because these worms can change their patterns dynamically. In this paper, a class of neighborhood-relation signatures (NRS) are proposed, including 1-NRS, 2-NRS and (1,2)-NRS. NRS can be used for detecting polymorphic worms since these worms often remain the same relationship between bytes in changing their patterns. Two signature generation algorithm based on expectation-maximization (EM) and Gibbs Sampling are designed to generate NRS. We perform extensive experiments to demonstrate the effectiveness of NRS and the correctness of the process of signatures generation. Experiment results show that our approach of defending polymorphic worm based on NRS is more effective than other approach based on existed signatures.

In order to prevent worms from propagating rapidly, it is essential to generate worm signatures quickly and accurately. However, most of recent approaches can not generate accurate signatures for polymorphic worms in environments with noise. In this paper, we present a signature generation algorithm, namely CCSF (color coding signature finding), for polymorphic worms based on color coding. CCSF divides n sequences into m groups and each group contains 20 sequences. Firstly, CCSF generates signatures for each group by adopting color coding and filters them. Then all reserved signatures are clustered to get rid of redundant substrings. In this approach, signature can be generated without any fragment in environments with noise, and it can be used in IDS (intrusion detection system) to detect polymorphic worm. We perform extensive experiments to demonstrate the effectiveness of our approach. Experiment results show distinct advantages in generating accurate signatures over other existed approaches.