Due to the complexity of annotation and inter-annotator variability, mos...
Code quality is a crucial construct in open-source software (OSS) with t...
Medical images like CT and MRI provide detailed information about the
in...
Deep learning-based medical image segmentation models suffer from perfor...
The hybrid architecture of convolution neural networks (CNN) and Transfo...
Manual medical image segmentation is subjective and suffers from
annotat...
Although recent years have witnessed the great success of convolutional
...
Glaucoma is one of the leading causes of irreversible blindness. Segment...
The universal model emerges as a promising trend for medical image
segme...
Fully annotated large-scale medical image datasets are highly valuable.
...
Modeling noise transition matrix is a kind of promising method for learn...
The domain discrepancy existed between medical images acquired in differ...
Medical image benchmarks for the segmentation of organs and tumors suffe...
Automated abdominal multi-organ segmentation is a crucial yet challengin...
Kidney structures segmentation is a crucial yet challenging task in the
...
Carotid vessel wall segmentation is a crucial yet challenging task in th...
In our previous work, i.e., HNF-Net, high-resolution feature representat...
Assessment of myocardial viability is essential in diagnosis and treatme...
Self-supervised learning (SSL) opens up huge opportunities for better
ut...
Deformable image registration is able to achieve fast and accurate align...
We propose a general framework (CEV Framework) for recommending and veri...
Manual annotation of medical images is highly subjective, leading to
ine...
In this paper, we proposed a novel mutual consistency network (MC-Net+) ...
The domain gap caused mainly by variable medical image quality renders a...
Automated and accurate segmentation of the infected regions in computed
...
In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module ...
Lung nodule malignancy prediction is an essential step in the early diag...
Convolutional neural networks (CNNs) have been the de facto standard for...
In this paper, we propose a Hybrid High-resolution and Non-local Feature...
Using radiological scans to identify liver tumors is crucial for proper
...
Differential Neural Architecture Search (NAS) methods represent the netw...
Automated and accurate 3D medical image segmentation plays an essential ...
It has been widely recognized that the success of deep learning in image...
Due to the intensive cost of labor and expertise in annotating 3D medica...
Analyzing the morphological attributes of blood vessels plays a critical...
Accurate and automated gland segmentation on histology tissue images is ...
Coronaviruses are important human and animal pathogens. To date the nove...
Assessing the location and extent of lesions caused by chronic stroke is...
Automated skin lesion segmentation on dermoscopy images is an essential ...
Gliomas are the most common primary brain malignancies, with different
d...
We recognize that the skin lesion diagnosis is an essential and challeng...
A multi-level deep ensemble (MLDE) model that can be trained in an 'end ...
Advanced deep learning methods have been developed to conduct prostate M...
Computer-aided techniques may lead to more accurate and more acces-sible...
Detection of pulmonary nodules on chest CT is an essential step in the e...
Recent years have witnessed the breakthrough success of deep convolution...
The Classification of medical images and illustrations in the literature...
Feature selection has always been a critical step in pattern recognition...