Manual medical image segmentation is subjective and suffers from
annotat...
Though achieving excellent performance in some cases, current unsupervis...
The universal model emerges as a promising trend for medical image
segme...
Recent developments in large language models (LLM) and generative AI hav...
In supervised learning for image denoising, usually the paired clean ima...
Modeling noise transition matrix is a kind of promising method for learn...
Medical image benchmarks for the segmentation of organs and tumors suffe...
As one of the most challenging and practical segmentation tasks, open-wo...
We propose a novel and unified method, measurement-conditioned denoising...
Program representation, which aims at converting program source code int...
We proved that a trained model in supervised deep learning minimizes the...
Forming a molecular candidate set that contains a wide range of potentia...
Self-supervised learning (SSL) opens up huge opportunities for better
ut...
Manual annotation of medical images is highly subjective, leading to
ine...
Lung nodule malignancy prediction is an essential step in the early diag...
Searching for novel molecules with desired chemical properties is crucia...
Convolutional neural networks (CNNs) have been the de facto standard for...
The expressiveness of deep neural network (DNN) is a perspective to
unde...
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...
Accurate and automated gland segmentation on histology tissue images is ...
Coronaviruses are important human and animal pathogens. To date the nove...
Wireless network optimization has been becoming very challenging as the
...
Automated skin lesion segmentation on dermoscopy images is an essential ...
In this paper, we propose a data-driven visual rhythm prediction method,...
A multi-level deep ensemble (MLDE) model that can be trained in an 'end ...
The Classification of medical images and illustrations in the literature...