The Large Language Models (LLMs) are poised to offer efficient and
intel...
Machine Learning-as-a-Service, a pay-as-you-go business pattern, is wide...
Millions of smart contracts have been deployed onto Ethereum for providi...
Being able to assess dog personality can be used to, for example, match
...
In this work, we propose a novel image reconstruction framework that dir...
Fueled by its successful commercialization, the recommender system (RS) ...
Zero-shot Chinese character recognition has attracted rising attention i...
Sparse inner product (SIP) has the attractive property of overhead being...
In this paper, we present VerifyML, the first secure inference framework...
Image representation is critical for many visual tasks. Instead of
repre...
In this paper, we study the problem of secure ML inference against a
mal...
In this paper, we address the problem of privacy-preserving federated ne...
Decentralized deep learning plays a key role in collaborative model trai...
3D reconstruction of pulmonary segments plays an important role in surgi...
Medical image synthesis has attracted increasing attention because it co...
Deep convolutional neural networks have proven to be remarkably effectiv...
A comprehensive representation of an image requires understanding object...
Automatic code generation is to generate the program code according to t...
Indoor environmental quality has been found to impact employees' product...
Novel multimodal imaging methods are capable of generating extensive, su...
In this study, we explore quantitative correlates of qualitative human e...
Radiomic representations can quantify properties of regions of interest ...
Multi-modal medical image segmentation plays an essential role in clinic...
Existing medical image super-resolution methods rely on pairs of low- an...
Segmentationand parcellation of the brain has been widely performed on b...
Tackling domain shifts in multi-centre and multi-vendor data sets remain...
Exploiting learning algorithms under scarce data regimes is a limitation...
Domain adaptation in healthcare data is a potentially critical component...
A fast non-convex low-rank matrix decomposition method for potential fie...
Early diagnosis of pathological invasiveness of pulmonary adenocarcinoma...
Analysis and modeling of the ventricles and myocardium are important in ...
Euler's elastica model has been extensively studied and applied to image...
Mammogram is the most effective imaging modality for the mass lesion
det...
Recent studies on medical image synthesis reported promising results usi...
Quantification of cerebral white matter hyperintensities (WMH) of presum...
Early diagnosis of pulmonary nodules (PNs) can improve the survival rate...
Brain image segmentation is used for visualizing and quantifying anatomi...
Gliomas are the most common primary brain malignancies, with different
d...
For many segmentation tasks, especially for the biomedical image, the
to...
Segmentation of both large and small white matter hyperintensities/lesio...
The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for e...
White matter hyperintensities (WMH) are commonly found in the brains of
...
Crowdsourcing provides a popular paradigm for data collection at scale. ...
Crowdsourcing has become an effective and popular tool for human-powered...
Crowdsourcing is an effective tool for human-powered computation on many...