Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are rare demyelinating diseases of the central nervous system. Limited sample sizes pose significant challenges for traditional convolutional neural networks (CNNs) in learning lesion features, particularly in identifying critical regions relevant to disease prediction. Due to the distinct lesion patterns between MS and NMOSD, the anterior visual pathway (AVP) plays a crucial role in early diagnosis. However, these lesions often exhibit low contrast, making them difficult to detect using conventional methods, despite their clearer representation in the frequency domain. Few studies have incorporated AVP as prior knowledge into deep learning frameworks or addressed the differences in lesion characteristics across the frequency domain. To tackle these challenges, we propose a multi-task network, VAE-FreqNet, which jointly performs AVP segmentation and disease classification. First, a dynamic decoupling strategy based on discrete cosine transform (DCT) is introduced, where the HFDownsample and HFTUpsample modules preserve high-frequency and low-frequency details lost during downsampling and upsampling, thereby enhancing subtle frequency-domain features. Additionally, we design a frequency-domain hierarchical variational autoencoder (FHVAE) module that employs HVAE blocks for variational inference, fusing decoupled and original features to generate synthetic representations, thus alleviating data scarcity. Extensive experiments demonstrate that VAE-FreqNet significantly improves both classification and segmentation performance.

Hemorrhagic transformation (HT) is a time-sensitive severe complication of endovascular thrombectomy for patients with ischemic stroke, and there is an urgent need to develop deep learning models to assist doctors in making rapid preliminary diagnoses. Currently popular Transformer deep learning architectures, while superior in modeling global relationships compared to traditional CNN, it still faces quadratic complexity issues when handling long sequences of medical images due to its inherent attention mechanism. In contrast, computational complexity of the Mamba model-based method grows linearly. Based on these findings, we have developed a novel Mamba model that effectively captures long-range dependencies and the sequential relationships among slices in high-dimensional medical image sequences. We evaluated the proposed model on a multi-center dataset. Experimental results show that our method outperforms other classical architectures and current advanced methods, validating the effectiveness and generalizability of the model composed of the aforementioned modules.

Machine learning based prediction in mobile networks is crucial for optimizing network operations. This paper presents a quantum-enhanced Tiny Time Mixer (QTTM), a novel hybrid model that integrates parameterized quantum circuits with quantum data re-uploading (QDR) into the classical TTM architecture, replacing its gated attention and prediction head modules. QDR enables QTTM to operate effectively on NISQ (Noisy Intermediate Scale Quantum) devices while maintaining enhanced representational power with reduced model size. Experimental results demonstrate that QTTM achieves performance parity with classical TTM in cellular traffic and user count prediction tasks, yet requires 23% fewer model parameters.

Hemorrhagic transformation (HT) is a severe complication of acute ischemic stroke (AIS) that can lead to disability or death. Accurate and timely risk assessment of HT is essential for clinicians to design effective treatment strategies. Previous studies in HT prediction have largely relied on machine learning and radiomics, which demand extensive manual data preprocessing by physicians. While some HT prediction models based on convolutional neural network (CNN) have been developed, they are limited in their ability to capture the sequential relationships between image slices and often lack the focus on crucial information. This study collected non-contrast computed tomography (NCCT) images and clinical data from 512 AIS patients across six hospitals to create a multi-center dataset. Based on the dataset, we propose CM-HTNet, a novel deep learning framework designed to predict HT risk in AIS patients following intravenous thrombolysis (IVT). CM-HTNet mimics the clinical process of reviewing NCCT images by focusing on key slices and integrating information from adjacent slices. It leverages CNNs to extract features from each NCCT slice and utilizes the Selective State-Space Model (SSM) within the Mamba framework to model sequential relationships between slices while prioritizing features relevant to HT prediction. Additionally, CM-HTNet incorporates the Neighborhood Rough Set (KRS) algorithm for clinical feature selection and cross-attention mechanisms to integrate clinical and imaging data for multimodal HT risk prediction. In testing on an external dataset from independent centers, CM-HTNet achieved a prediction accuracy of 88.85% and an AUC of 95.17%, showcasing its strong performance and generalization capabilities.

The degree of establishment of cerebrovascular collateral circulation is closely related to the prognosis of patients with acute ischemic stroke, but the evaluation of collateral circulation requires high professional experience of physicians because of the complex structure of the cerebral vessels themselves, and the variety of scoring criteria resulting in poor consistency of results between physicians. Therefore, the use of computer-aided diagnostic techniques to evaluate the establishment of collateral circulation in patients with ischemic stroke is of great clinical importance. In this paper, we proposed a novel method for automatic scoring of collateral circulation via multiphase cranial CTA (computed tomography angiography) to assist physicians in diagnosis. We compared with existing mainstream classification n etworks, our method is able to achieve 90.43% accuracy. Further, the effectiveness of the method was further validated by ablation experiments. However, the multi-phase Cranial CTA collateral circulation scoring algorithm based on a feature fusion network with the hybrid attention mechanism effectively improves the efficiency of prognostic judgment, avoids the limitations of manual extraction of image features in the traditional ways, and plays an auxiliary role in diagnosis for physicians in clinical practice, which is useful for guiding the decision of clinical syndromes in lateral branch circulation stroke.

Acute ischemic stroke (AIS) caused by large vessel occlusion (LVO) has high disability and mortality. However, due to the individual differences of physiological structure and pathological changes between patients, it will be difficult to detect the occluded vessels, so as to delay the treatment timing. Therefore, it is of great significance to a ssist d octors to locate occluded vessels quickly and accurately in clinical practice. In this paper, we present a novel multi-branch hybrid network (MBH-Net) with auxiliary attention guidance to detect occluded vessels. The proposed network consists of three branches for universal representation learning, patient representation learning and classifier learning, respectively. Furthermore, we propose a semantic feature enhancement module to extract more robust semantic information. Particularly, we introduce an auxiliary attention guidance module to guide the attention tendency of MBH-Net, which can make the network give a more reasonable visual interpretation. Extensive experiments show that our MBH-Net can achieve satisfactory accuracy and give a reasonable visual interpretation.

Medical imaging is widely used in hospital clinical workflows. Assisting physicians in diagnosis by automatically generating reports from radiological images is an unmet clinical demand and requires urgent attention. However, this task suffers from two significant problems: 1) visual and textual data biases, and 2) the Transformer decoder makes no distinction between visual and non-visual words. We propose a novel multi-task approach combining natural language processing with machine learning techniques to meet this clinical need, i.e., creating fluent and accurate radiology reports. We name our system as Multi-modal Adaptive Transformer (MATNet), which consists of three key modules. First, Multi-Modal Encoder (MME) explores the relationship between radiology images and clinical notes. Second, Disease Classifier (DC) classifies the states of each disease topic and provides state-aware disease embeddings to alleviate visual data bias. Last, Adaptive Decoder (AD) dynamically measures the contribution of source signals and target signals when generating the next word. Based on our evaluations using benchmark IU-XRay and MIMIC-CXR datasets, the proposed MATNet outperformed previous state-of-the-art models on language fluency and clinical accuracy metrics such as BLEU scores.

Abstract As a crucial factor, population diversity greatly affects performances of swarm intelligence algorithms. Especially, for large‐scale optimization problems (LSOPs), the searching space is huge and the number of local optima dramatically increases. Hence to well address LSOPs, a healthy population diversity is helpful to prevent a swarm from premature convergence. However, this is a big challenge to balance exploration and exploitation for swarm intelligence algorithms. To handle with this issue, in this paper, we design a novel algorithm structure for swarm update. In the proposed algorithm, a swarm is divided into several groups and conduct competition in each group where the loser will learn from the winner and meanwhile the winner does nothing in the corresponding iteration. For diversity measurement, we abandon the distance‐based measurement, but employ a frequency‐based measurement, namely entropy indicator, so that the diversity maintenance can be conducted with a different measurement of convergence situation. In this way, the diversity maintenance and convergence can be conducted simultaneously and independently. The benchmarks on the suite of LSOPs are employed to validate the performance of a proposed algorithm. By comparing several state‐of‐the‐art competitor algorithms, the results demonstrate that the proposed algorithm is effective and competitive in dealing with LSOPs.