When performing remote sensing image segmentation, practitioners often encounter various
challenges, such as a strong imbalance in the foreground–background, the presence of tiny …

Efficient polyp segmentation in healthcare plays a critical role in enabling early diagnosis of
colorectal cancer. However, the segmentation of polyps presents numerous challenges …

Capsule network is a recent new deep network architecture that has been applied
successfully for medical image segmentation tasks. This work extends capsule networks for …

In the field of chest X-ray (CXR) diagnosis, existing works often focus solely on determining
where a radiologist looks, typically through tasks such as detection, segmentation, or …

Automated segmentation of liver tumors on CT scans is essential for aiding diagnosis and
assessing treatment. Computer-aided diagnosis can reduce the costs and errors associated …

Convolutional Neural Networks (CNNs) have achieved promising results in medical image
segmentation. However, CNNs require lots of training data and are incapable of handling …

The prerequisite for the use of remote sensing images is that their security must be
guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing …

The analysis and segmentation of articular cartilage magnetic resonance (MR) images
belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal …

Chemotherapy remains controversial for stage II nasopharyngeal carcinoma because of its
considerable prognostic heterogeneity. We aimed to develop an MRI-based deep learning …

In the food industry, assessing the quality of poultry carcasses during processing is a crucial
step. This study proposes an effective approach for automating the assessment of carcass …