Starting from the resurgence of deep learning, vision-language models (V...
Accurate automatic segmentation of medical images typically requires lar...
Automatic examination of thin-prep cytologic test (TCT) slides can assis...
Numerous studies have underscored the significant privacy risks associat...
Model pre-training on large text corpora has been demonstrated effective...
The potential of integrating Computer-Assisted Diagnosis (CAD) with Larg...
Recent self-supervised contrastive learning methods greatly benefit from...
When communicating with elders with cognitive impairment, cognitive
stim...
Mammographic image analysis is a fundamental problem in the computer-aid...
Magnetic resonance (MR) images collected in 2D scanning protocols typica...
The recent progress of large language models (LLMs), including ChatGPT a...
Large language models (LLMs) have recently demonstrated their potential ...
Language models pre-trained on scientific literature corpora have
substa...
Medical image segmentation methods are generally designed as fully-super...
One-shot segmentation of brain tissues is typically a dual-model iterati...
Collaborative Edge Computing (CEC) is an effective method that improves ...
Recent lay language generation systems have used Transformer models trai...
Heterogeneous Information Network (HIN) is essential to study complicate...
Objective For the UK Biobank standardized phenotype codes are associated...
Brain network analysis for traumatic brain injury (TBI) patients is crit...
We propose a novel framework for Alzheimer's disease (AD) detection usin...
RNA structure determination and prediction can promote RNA-targeted drug...
Gene Ontology (GO) is the primary gene function knowledge base that enab...
We study the capability of arbitrage-free neural-SDE market models to yi...
Learning harmful shortcuts such as spurious correlations and biases prev...
Magnetic resonance (MR) images are often acquired in 2D settings for rea...
Discovering latent topics from text corpora has been studied for decades...
When deep neural network (DNN) was first introduced to the medical image...
There is a resurgence of interest in Byzantine fault-tolerant (BFT) syst...
In this paper, we examine the capacity of an arbitrage-free neural-SDE m...
Plant phenotyping (Guo et al. 2021; Pieruschka et al. 2019) focuses on
s...
Knee osteoarthritis (OA) is the most common osteoarthritis and a leading...
With the increasingly available large-scale cancer genomics datasets, ma...
Lesion detection is a fundamental problem in the computer-aided diagnosi...
Medical report generation, which aims to automatically generate a long a...
Registration of brain MRI images requires to solve a deformation field, ...
Precisely defining the terminology is the first step in scientific
commu...
Recent research has witnessed advances in facial image editing tasks
inc...
Modelling joint dynamics of liquid vanilla options is crucial for
arbitr...
Accurate protein structure prediction from amino-acid sequences is criti...
Proteins structure prediction has long been a grand challenge over the p...
The integration of Mobile Edge Computing (MEC) and Wireless Power Transf...
In this paper, we make a first attempt to incorporate both commuting dem...
Learned indices have been proposed to replace classic index structures l...
This paper presents a thorough evaluation of the existing methods that
a...
We present a fully automatic system that can produce high-fidelity,
phot...
This paper introduces an enhanced meta-heuristic (ML-ACO) that combines
...
Recently, Batch DropBlock network (BDB) has demonstrated its effectivene...
Recent research has witnessed the advances in facial image editing tasks...
Computer-aided diagnosis with deep learning techniques has been shown to...