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View article: Multi‐View Seizure Classification Based on Attention‐Based Adaptive Graph ProbSparse Hybrid Network
Multi‐View Seizure Classification Based on Attention‐Based Adaptive Graph ProbSparse Hybrid Network Open
Epilepsy is a neurological disorder characterised by recurrent seizures due to abnormal neuronal discharges. Seizure detection via EEG signals has progressed, but two main challenges are still encountered. First, EEG data can be distorted …
View article: HGTS-Former: Hierarchical HyperGraph Transformer for Multivariate Time Series Analysis
HGTS-Former: Hierarchical HyperGraph Transformer for Multivariate Time Series Analysis Open
Multivariate time series analysis has long been one of the key research topics in the field of artificial intelligence. However, analyzing complex time series data remains a challenging and unresolved problem due to its high dimensionality…
View article: Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation
Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation Open
Although SAM-based single-source domain generalization models for medical image segmentation can mitigate the impact of domain shift on the model in cross-domain scenarios, these models still face two major challenges. First, the segmentat…
View article: Bridging Vision and Language: Optimal Transport-Driven Radiology Report Generation via LLMs
Bridging Vision and Language: Optimal Transport-Driven Radiology Report Generation via LLMs Open
Radiology report generation represents a significant application within medical AI, and has achieved impressive results. Concurrently, large language models (LLMs) have demonstrated remarkable performance across various domains. However, e…
View article: Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning
Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning Open
Modeling label correlations has always played a pivotal role in multi-label image classification (MLC), attracting significant attention from researchers. However, recent studies have overemphasized co-occurrence relationships among labels…
View article: Bidirectional Uncertainty-Aware Region Learning for Semi-Supervised Medical Image Segmentation
Bidirectional Uncertainty-Aware Region Learning for Semi-Supervised Medical Image Segmentation Open
In semi-supervised medical image segmentation, the poor quality of unlabeled data and the uncertainty in the model's predictions lead to models that inevitably produce erroneous pseudo-labels. These errors accumulate throughout model train…
View article: XiHeFusion: Harnessing Large Language Models for Science Communication in Nuclear Fusion
XiHeFusion: Harnessing Large Language Models for Science Communication in Nuclear Fusion Open
Nuclear fusion is one of the most promising ways for humans to obtain infinite energy. Currently, with the rapid development of artificial intelligence, the mission of nuclear fusion has also entered a critical period of its development. H…
View article: Text-Region Matching for Multi-Label Image Recognition with Missing Labels
Text-Region Matching for Multi-Label Image Recognition with Missing Labels Open
Recently, large-scale visual language pre-trained (VLP) models have demonstrated impressive performance across various downstream tasks. Motivated by these advancements, pioneering efforts have emerged in multi-label image recognition with…
View article: Domain Adaptive Lung Nodule Detection in X-ray Image
Domain Adaptive Lung Nodule Detection in X-ray Image Open
Medical images from different healthcare centers exhibit varied data distributions, posing significant challenges for adapting lung nodule detection due to the domain shift between training and application phases. Traditional unsupervised …
View article: Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens
Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens Open
Many RGBT tracking researches primarily focus on modal fusion design, while overlooking the effective handling of target appearance changes. While some approaches have introduced historical frames or fuse and replace initial templates to i…
View article: Hybrid attention mechanism of feature fusion for medical image segmentation
Hybrid attention mechanism of feature fusion for medical image segmentation Open
Traditional convolution neural networks (CNN) have achieved good performance in multi‐organ segmentation of medical images. Due to the lack of ability to model long‐range dependencies and correlations between image pixels, CNN usually igno…
View article: Semantic-Aware Dual Contrastive Learning for Multi-Label Image Classification
Semantic-Aware Dual Contrastive Learning for Multi-Label Image Classification Open
Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on …
View article: Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification
Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification Open
Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on …
View article: Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation
Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation Open
Manual image segmentation consumes time. An automatic and accurate method to segment multimodal brain tumors using context information rich three-dimensional medical images that can be used for clinical treatment decisions and surgical pla…
View article: Learning to Cluster Faces via Hypergraph Convolution with Transformer on Large Graph
Learning to Cluster Faces via Hypergraph Convolution with Transformer on Large Graph Open
Face clustering is a very useful method in image annotation, image retrieval and other real-world applications.The main challenge is that with the increasing data scale, the large graph constructed by KNN is difficult to train due to out o…
View article: Targeting non-coding RNA H19: A potential therapeutic approach in pulmonary diseases
Targeting non-coding RNA H19: A potential therapeutic approach in pulmonary diseases Open
Non-coding RNA is still one of the most popular fields in biology research. In recent years, people paid more attention to the roles of H19 in lung diseases, which expressed abnormally in various pathological process. Therefore, this revie…
View article: Confidence-Based Simple Graph Convolutional Networks for Face Clustering
Confidence-Based Simple Graph Convolutional Networks for Face Clustering Open
Face clustering is an effective method for taking advantage of unlabeled face data. Recent studies use graph convolutional networks (GCNs) to learn feature embeddings from the neighborhood information between face images. However, most of …
View article: Link Selection with Collaborative Low-rank and Sparse Factorization for Community Detection in Multiplex Networks
Link Selection with Collaborative Low-rank and Sparse Factorization for Community Detection in Multiplex Networks Open
With the advent of multiple types of proximities between nodes, multiplex networks have emerged widely in the real world and been attracting increasing attention recently.The existing researches on community detection in multiplex networks…
View article: Overlapping Community Detection based on Network Decomposition
Overlapping Community Detection based on Network Decomposition Open
Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent d…