With the improvement of hardware computing power and the development of deep learning
algorithms, a revolution of" artificial intelligence (AI)+ medical image" is taking place …

Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent
years, which has good applications in medical image analysis. In this paper, DenseNet is …

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from the labeled
source domain to the unlabeled target domain. Existing self-training based UDA approaches …

Accurate wind power forecasting is of great significance for power system operation. In this
study, a triple-stage multi-step wind power forecasting approach is proposed by applying …

Recently, many computer-aided diagnosis (CAD) methods have been proposed to help
physicians automatically classify endoscopic images. However, most existing methods often …

Automatic semantic segmentation in 2D echocardiography is vital in clinical practice for
assessing various cardiac functions and improving the diagnosis of cardiac diseases …

The automatic segmentation of polyp in endoscopy images is crucial for early diagnosis and
cure of colorectal cancer. Existing deep learning-based methods for polyp segmentation …

Purpose Wireless capsule endoscopy (WCE) is a fundamental diagnosing tool for gastro-
intestinal (GI) lesion detection. Detecting and locating the lesions in WCE images using a …

Supervised machine learning requires training on the dataset with annotation. However, fine-
grained annotation is very expensive to acquire. In the medical image analysis domain, the …

Objectives To develop and validate a deep learning model for predicting overall survival
from whole-brain MRI without tumor segmentation in patients with diffuse gliomas. Methods …