In Time-Triggered (TT) or time-sensitive networks, the transmission of a...
Harnessing the power of pre-training on large-scale datasets like ImageN...
We propose a novel neural deformable model (NDM) targeting at the
recons...
Prompt engineering is an essential technique for enhancing the abilities...
Federated learning is a popular collaborative learning approach that ena...
This paper studies the leader-following consensuses of uncertain and
non...
Pause insertion, also known as phrase break prediction and phrasing, is ...
The increasing availability of audio editing software altering digital a...
Artificial Intelligence (AI) is having a tremendous impact across most a...
Federated learning (FL) enables the building of robust and generalizable...
Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platf...
Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a
...
Ischemic Stroke Lesion Segmentation challenge (ISLES 2022) offers a plat...
Which volume to annotate next is a challenging problem in building medic...
The Kalman filter has been adopted in acoustic echo cancellation due to ...
Vision Transformers (ViT)s have recently become popular due to their
out...
Cross-silo federated learning (FL) has attracted much attention in medic...
Federated learning (FL) is a distributed machine learning technique that...
Semantic segmentation of brain tumors is a fundamental medical image ana...
Semantic segmentation of 3D medical images is a challenging task due to ...
Vision Transformers (ViT)s have shown great performance in self-supervis...
Lesion segmentation in medical imaging has been an important topic in
cl...
Multiple instance learning (MIL) is a key algorithm for classification o...
Federated learning (FL) for medical image segmentation becomes more
chal...
Building robust deep learning-based models requires diverse training dat...
Deep learning models for medical image segmentation are primarily
data-d...
Federated learning (FL) enables collaborative model training while prese...
Pre-trained models, e.g., from ImageNet, have proven to be effective in
...
Recently, neural architecture search (NAS) has been applied to automatic...
We analyzed Medical Subject Headings (MeSH) from 21.6 million research
a...
Fully Convolutional Neural Networks (FCNNs) with contracting and expansi...
Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard ...
Under various poses and heavy occlusions,3D hand model reconstruction ba...
Active learning is a unique abstraction of machine learning techniques w...
The recent outbreak of COVID-19 has led to urgent needs for reliable
dia...
The performance of deep learning-based methods strongly relies on the nu...
The training of deep learning models typically requires extensive data, ...
Medical image annotation is a major hurdle for developing precise and ro...
Current deep learning paradigms largely benefit from the tremendous amou...
The accurate reconstruction of under-sampled magnetic resonance imaging ...
The 3D volumetric shape of the heart's left ventricle (LV) myocardium (M...
Knowledge graph embedding, which aims to learn the low-dimensional
repre...
Although having achieved great success in medical image segmentation, de...
Deep neural network (DNN) based approaches have been widely investigated...
Object segmentation plays an important role in the modern medical image
...
The topological structure of skeleton data plays a significant role in h...
In this paper we report the challenge set-up and results of the Large Sc...
3D convolution neural networks (CNN) have been proved very successful in...
Automatic segmentation of abdomen organs using medical imaging has many
...
In this work, we attempt the segmentation of cardiac structures in late
...