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 …

This study proposes a lightweight automatic 3D algorithm with an attention mechanism for
the segmentation of brain-tumor images to address the challenges. Accurate segmentation …

Rib segmentation based on chest X-ray images is essential in the computer-aided diagnosis
systems of lung cancer, which serves as an important step in the quantitative analysis of …

One of the areas with the potential to be explored in quantum computing (QC) is machine
learning (ML), giving rise to quantum machine learning (QML). In an era when there is so …

Accurate segmentation of multiple targets, such as ribs, clavicles, heart, and lung fields, from
chest X-ray images is crucial for diagnosing various lung diseases. Currently, mainstream …

Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a
decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative …

Background and objective The delineation of the lumen contour and external elastic lamina
(EEL) in intravascular ultrasound (IVUS) images is crucial for the quantitative analysis of …

Purpose Accurately segmenting organs‐at‐risk (OARs) is a key step in the effective planning
of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. In OAR segmentation of …

Automatic identification of abnormal and normal cells is a critical step in computer-assisted
pathology, owing to certain heterogeneous characteristics of cancer cells. However …

Image processing technologies and computer-aided diagnosis are medical technologies
used to support decision-making processes of radiologists and medical professionals who …