The objective of semantic segmentation in cross-modal medicine is to align the distribution
among different domains. The images from different domains contain various styles and …

Convolutional neural networks have been widely used in image semantic segmentation.
However, continuous downsampling operations in convolutional neural networks (such as …

Precise delineation of multiple organs or abnormal regions in the human body from medical
images plays an essential role in computer-aided diagnosis, surgical simulation, image …

By exploiting the complementary information of RGB modality and thermal modality, RGB-
thermal (RGB-T) semantic segmentation is robust to adverse lighting conditions. When …

Image segmentation is essential to medical image analysis as it provides the labeled
regions of interest for the subsequent diagnosis and treatment. However, fully-supervised …

With the development of image segmentation technology, image context information plays
an increasingly important role in semantic segmentation. However, due to the complexity of …

Image segmentation, often based on the properties of image pixels, is a widely used method
in digital image processing and analysis to divide an image into multiple parts or areas. The …

Deep learning methods have been successfully applied to Brain tumor segmentation.
However, the extreme data imbalance exists in the different sub-regions of tumor, results in …

Deep neural networks have recently been succeessful in the field of medical image
segmentation; however, they are typically subject to performance degradation problems …

(MIS) using Deep Neural Networks (DNNs) has achieved significant performance
improvements and holds great promise for future developments. This paper presents a …