Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But, the application of deep learning in medical image analysis is limited by ...
详细信息
Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But, the application of deep learning in medical image analysis is limited by the scarcity of high-quality annotated medical imaging data. An emerging solution is self-supervised learning (SSL), among which contrastive SSL is the most successful approach to rivalling or outperforming supervised learning. This review investigates several state-of-the-art contrastive SSL algorithms originally on natural images as well as their adaptations for medical images, and concludes by discussing recent advances, current limitations, and future directions in applying contrastive SSL in the medical domain.
Most existing unsupervised domain adaptation (UDA) methods primarily focus on single-source and single-target domain scenarios, referred to as single-source unsupervised domain adaptation. However, in practical applic...
详细信息
The growing dependence on technology in healthcare has resulted in the creation of sophisticated hospital networks that are highly linked and vulnerable to cyber threats. A reliable Network Intrusion Detection System ...
详细信息
Abstractive Multi-Document Summarization (MDS) is a crucial technique in cognitive computing, enabling the efficient synthesis of a documents cluster into a concise and complete summary. Despite recent advances, exist...
详细信息
In view of the impact of uncertain factors faced by Virtual Reality (VR) video services in the Internet of Vehicles, if the impact of different uncertain factors can be identified, targeted optimization can be made to...
详细信息
This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurat...
详细信息
Climate change poses significant challenges to societies worldwide, necessitating accurate and reliable climate prediction models to inform mitigation and adaptation strategies. The ability to forecast climate variabl...
详细信息
The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the development and implementation of a dee...
详细信息
Discovering causality from multivariate time series is an important but challenging *** existing methods focus on estimating the Granger causal structures among multivariate time series,while ignoring the prior knowle...
详细信息
Discovering causality from multivariate time series is an important but challenging *** existing methods focus on estimating the Granger causal structures among multivariate time series,while ignoring the prior knowledge of these time series, e.g., the group of the time series. Focusing on discovering the Granger causal structures among groups of time series, we propose a Granger causal representation learning method to solve this problem. First, we use the multiset canonical correlation analysis method to learn the Granger causal representation of each group of time series. Then, we model the Granger causal relationships among the learned Granger causal representations using a recurrent neural network with temporal information. Finally, we formulate the above two stages into one unified optimization problem, which is efficiently solved using the augmented Lagrangian method. We conduct extensive experiments on synthetic and real-world datasets to validate the correctness and effectiveness of the proposed method.
Point cloud sequence-based 3D action recognition has achieved impressive performance and efficiency. However, existing point cloud sequence modeling methods cannot adequately balance the precision of limb micro-moveme...
详细信息
暂无评论