In this paper, we propose a bi-modality medical image synthesis approach...
Atrial Fibrillation (AF) is characterized by rapid, irregular heartbeats...
Food waste presents a substantial challenge with significant environment...
Despite the remarkable performance of Vision Transformers (ViTs) in vari...
The expanding model size and computation of deep neural networks (DNNs) ...
Federated noisy label learning (FNLL) is emerging as a promising tool fo...
Limited training data and severe class imbalance impose significant
chal...
Medical image segmentation is a fundamental task in the community of med...
This study investigates barely-supervised medical image segmentation whe...
Medical image segmentation has made significant progress in recent years...
Multiple instance learning (MIL) has emerged as a popular method for
cla...
The advent of Vision Transformer (ViT) has brought substantial advanceme...
Optical Coherence Tomography Angiography (OCTA) has become increasingly ...
Deep regression is an important problem with numerous applications. Thes...
Weight oscillation is an undesirable side effect of quantization-aware
t...
The explosive growth of various types of big data and advances in AI
tec...
Left-ventricular ejection fraction (LVEF) is an important indicator of h...
Medical anomaly detection is a crucial yet challenging task aiming at
re...
Over the past few years, the rapid development of deep learning technolo...
Capturing the long-range dependencies has empirically proven to be effec...
This paper studies the few-shot skin disease classification problem. Bas...
Nuclei Segmentation from histology images is a fundamental task in digit...
Vision transformers have recently set off a new wave in the field of med...
Convolutional neural networks (CNN), the most prevailing architecture fo...
Federated learning (FL), enabling different medical institutions or clie...
Chest X-ray (CXR) is the most typical radiological exam for diagnosis of...
The purpose of federated learning is to enable multiple clients to joint...
Nuclei segmentation is a crucial task for whole slide image analysis in
...
This paper explores the feasibility of finding an optimal sub-model from...
Image regression tasks for medical applications, such as bone mineral de...
This paper aims to explore the feasibility of neural architecture search...
The nonuniform quantization strategy for compressing neural networks usu...
This paper presents a simple yet effective two-stage framework for
semi-...
AIoT processors fabricated with newer technology nodes suffer rising sof...
Deformable convolution networks (DCNs) proposed to address the image
rec...
The best performing Binary Neural Networks (BNNs) are usually attained u...
Hardware faults on the regular 2-D computing array of a typical deep lea...
This work aims to empirically clarify a recently discovered perspective ...
Previous studies dominantly target at self-supervised learning on real-v...
The goal of few-shot learning is to learn a classifier that can recogniz...
We present a new learning-based approach to recover egocentric 3D vehicl...
End-to-end deep representation learning has achieved remarkable accuracy...
We present joint multi-dimension pruning (named as JointPruning), a new
...
Binary Neural Networks (BNNs), known to be one among the effectively com...
In this paper, we propose several ideas for enhancing a binary network t...
Residual representation learning simplifies the optimization problem of
...
Optimization of Binarized Neural Networks (BNNs) currently relies on
rea...
Deep neural decision forest (NDF) achieved remarkable performance on var...
In this paper, we address the problem of synthesizing multi-parameter
ma...
In this paper, we study 1-bit convolutional neural networks (CNNs), of w...