Learning a deep model from small data is an opening and challenging problem. In high-dimensional spaces, few samples only occupy an extremely small portion of the space, often exhibiting sparsity issues. Classifying in this globally sparse sample spa...
Most disconnector fault diagnosis methods have high accuracy in model training. However, it is a challenging task to maintain high accuracy, a faster diagnosis speed, and less computation in practical situations. In this paper, we propose a multi-gra...
With the explosion of information, the amount of text data has increased significantly, making text categorization a central area of research in natural language processing (NLP). Traditional machine learning methods are effective, but deep learning...
Address is a structured description used to identify a specific place or point of interest, and it provides an effective way to locate people or objects. The standardization of Chinese place name and address occupies an important position in the cons...
The traditional spectrum sensing algorithm based on deep learning requires a large number of labeled samples for model training, but it is difficult to obtain them in the actual sensing scene. This paper applies self-supervised contrast learning in o...
Machine learning has gained significant recognition as a powerful approach for medical diagnosis using medical images. Among various medical imaging modalities, contrast-enhanced CT (CECT) is utilized to obtain additional diagnostic information that...
Self-supervised contrastive learning has demonstrated remarkable effectiveness in learning visual representations without labeled data, yet its application to 3D local feature learning from point clouds remains underexplored. Existing methods predomi...
The current deep learning (DL)-based Specific Emitter Identification (SEI) methods rely heavily on the training of massive labeled data during the training process. However, the lack of labeled data in a real application would lead to a decrease in t...
Event detection is a crucial task in information extraction. Existing research primarily focuses on machine automatic detection tasks, which often perform poorly in certain practical applications. To address this, an interactive event-detection mode...
Unsupervised person re-identification has attracted a lot of attention due to its strong potential to adapt to new environments without manual annotation, but learning to recognise features in disjoint camera views without annotation is still challen...
The rapid development of artificial intelligence offers more opportunities for intelligent mechanical diagnosis. Fault diagnosis of wind turbines is beneficial to improve the reliability of wind turbines. Due to various reasons, such as difficulty in...
Burn injuries are a common traumatic condition, and the early diagnosis of burn depth is crucial for reducing treatment costs and improving survival rates. In recent years, image-based deep learning techniques have been utilized to realize the automa...
Objectives: Breast cancer is one of the most common malignant tumors among women worldwide, and accurate assessment of axillary lymph node metastasis (ALNM) is crucial for determining treatment strategies. Compared to conventional ultrasound, contras...
Gadolinium-based contrast agents (GBCAs) are vital for glioma imaging yet pose safety, cost, and accessibility issues; predicting contrast enhancement from non-contrast MRI via machine learning (ML) provides a safer, economical alternative, as enhanc...
Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by significant neurological plasticity in early childhood, where timely interventions like behavioral therapy, language training, and social skills development...
Background/Objectives: Aortic dissection (AD) and aortic intramural hematoma (IMH) are fatal diseases with similar clinical characteristics. Immediate computed tomography (CT) with a contrast medium is required to confirm the presence of AD or IMH. T...
The detection of contrast-enhancing lesions (CELs) is fundamental for the diagnosis and monitoring of patients with multiple sclerosis (MS). This task is time-consuming and suffers from high intra- and inter-rater variability in clinical practice. Ho...
Total Kidney Volume (TKV) is essential for analyzing the progressive loss of renal function in Autosomal Dominant Polycystic Kidney Disease (ADPKD). Conventionally, to measure TKV from medical images, a radiologist needs to localize and segment the k...
Contrast-enhanced ultrasound (CEUS) is a promising imaging modality in predicting the efficacy of neoadjuvant chemotherapy for pancreatic cancer, a tumor with high mortality. In this study, we proposed a deep-learning-based strategy for analyzing CEU...
In order to automate defect detection with few samples using unsupervised learning, this paper, considering materials commonly used in aircraft, proposes a phased array ultrasonic detection defect identification method using non-defect samples for tr...
In this study, we considered preoperative prediction of microvascular invasion (MVI) status with deep learning (DL) models for patients with early-stage hepatocellular carcinoma (HCC) (tumor size ≤ 5 cm). Two types of DL models based only on venou...
A keloid results from abnormal wound healing, which has different blood perfusion and growth states among patients. Active monitoring and treatment of actively growing keloids at the initial stage can effectively inhibit keloid enlargement and has im...
In recent years, contrastive learning has been a highly favored method for self-supervised representation learning, which significantly improves the unsupervised training of deep image models. Self-supervised learning is a subset of unsupervised lear...
Background: Contrast-enhanced ultrasound (CEUS) is an important imaging modality in the diagnosis of liver tumors. By using contrast agent, a more detailed image is obtained. Time-intensity curves (TIC) can be extracted using a specialized software,...
Background/Objectives: This study aimed to assess the impact of a deep learning-based iodine contrast augmentation (DLCA) algorithm on image quality and diagnostic performance for pulmonary embolism (PE) detection in suboptimally enhanced CT pulmonar...
Background: To validate the automated quantification of cardiac chamber volumes and myocardial mass on non-contrast chest CT using cardiac MR (CMR) as a reference. Methods: We retrospectively included 53 consecutive patients who received non-contrast...
Background and Objectives: Contrast-enhanced mammography (CEM) provides both morphological and functional information and may reflect breast cancer biology similarly to Magnetic Resonance Imaging (MRI). Materials and Methods: This single-center retro...
Graph Contrastive Learning (GCL) seeks to learn nodal or graph representations that contain maximal consistent information from graph-structured data. While node-level contrasting modes are dominating, some efforts have commenced to explore consisten...
Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream...
Learning effective graph representations in an unsupervised manner is a popular research topic in graph data analysis. Recently, contrastive learning has shown its success in unsupervised graph representation learning. However, how to avoid collapsin...
Objectives: Differentiation of hyperdense areas on non-contrast computed tomography (NCCT) images as hemorrhagic transformation (HT) and contrast accumulation (CA) after endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) patients are crit...
Data sparsity caused by limited interactions makes it challenging for recommendation to accurately capture user preferences. Contrastive learning effectively alleviates this issue by enriching embedding information through the learning of diverse con...
Contrastive learning is a machine learning technique in which models learn by contrasting similar and dissimilar data points. Its goal is to learn a representation of data in such a way that similar instances are close together in the representation...
The existing learning resource recommendation systems suffer from data sparsity and missing data labels, leading to the insufficient mining of the correlation between users and courses. To address these issues, we propose a learning resource recommen...
Graph clustering is a fundamental and challenging task in unsupervised learning. It has achieved great progress due to contrastive learning. However, we find that there are two problems that need to be addressed: (1) The augmentations in most graph c...
Background and Objectives: Contrast-associated acute kidney injury (CA-AKI) is a frequent and clinically significant complication in patients with acute myocardial infarction (AMI) undergoing coronary angiography. Early and accurate risk stratificati...
Mucus plugs are airway-obstructing accumulations of inspissated secretions frequently observed in obstructive lung diseases (OLDs), including chronic obstructive pulmonary disease (COPD), severe asthma, and cystic fibrosis. Their presence on chest CT...
As one of the most important components in rotating machinery, if bearings fail, serious disasters may occur. Therefore, the remaining useful life (RUL) prediction of bearings is of great significance. Health indicator (HI) construction and early fau...
The semantic segmentation (SS) of low-contrast images (LCIs) remains a significant challenge in computer vision, particularly for sensor-driven applications like medical imaging, autonomous navigation, and industrial defect detection, where accurate...
Due to the success observed in deep neural networks with contrastive learning, there has been a notable surge in research interest in graph contrastive learning, primarily attributed to its superior performance in graphs with limited labeled data. Wi...
This paper proposes a method called Grouped Contrastive Learning of self-supervised Sentence Representation (GCLSR), which can learn an effective and meaningful representation of sentences. Previous works maximize the similarity between two vectors t...
Although deep learning algorithms have achieved significant progress in a variety of domains, they require costly annotations on huge datasets. Self-supervised learning (SSL) using unlabeled data has emerged as an alternative, as it eliminates manual...
Lifelong learning portrays learning gradually in nonstationary environments and emulates the process of human learning, which is efficient, robust, and able to learn new concepts incrementally from sequential experience. To equip neural networks with...
Background: Intracranial hemorrhage (ICH) is a life-threatening medical condition that needs early detection and treatment. In this systematic review and meta-analysis, we aimed to update our knowledge of the performance of deep learning (DL) models...
Generating high-quality programming exercises with well-aligned problem descriptions, test cases, and code solutions is crucial for computer science education. However, current methods often lack coherence among these components, reducing their educa...
Font is a crucial design aspect, however, classifying fonts is challenging compared with that of other natural objects, as fonts differ from images. This paper presents the application of contrastive learning in font style classification. We conducte...
With the development of remote sensing technology, the continuing accumulation of remote sensing data has brought great challenges to the remote sensing field. Although multiple deep-learning-based classification methods have made great progress in s...
To address the limitations of existing graph contrastive learning methods, which fail to adaptively integrate feature and topological information and struggle to efficiently capture multi-hop information, we propose an adaptive multi-view parallel gr...
Multimodal sentiment analysis (MSA) has attracted more and more attention in recent years. This paper focuses on the representation learning of multimodal data to reach higher prediction results. We propose a model to assist in learning modality repr...
Supervised contrastive learning optimizes a loss that pushes together embeddings of points from the same class while pulling apart embeddings of points from different classes. Class collapse—when every point from the same class has the same emb...
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