Background The deterministic deep learning models have achieved state-of-the-art
performance in various medical image analysis tasks, including nuclei segmentation from …

Nuclei segmentation is an initial step in the automated analysis of digitized microscopic
images. This paper focuses on utilizing the LinkNET-34 architecture for semantic …

Challenges in accurate tumor detection paves the way to haste the improvement of solid
kidney tumor semantic segmentation methodologies. Accurate segmentation of kidney tumor …

Nuclei segmentation is a fundamental but challenging task in histopathological image
analysis. One of the main problems is the existence of overlapping regions which increases …

Background Efficient computational recognition and segmentation of target organ from
medical images are foundational in diagnosis and treatment, especially about pancreas …

Accurate segmentation of nuclei plays a critical role in pathology since assessments and
diagnoses are mainly based on the recognition, measurement, and counting of nuclei …

Histopathology image segmentation is a significant step in the early detection of diseases.
Compared to traditional segmentation methods, deep learning models provide better …

The advent of digital pathology provides us with the challenging opportunity to automatically
analyze whole slides of diseased tissue in order to derive quantitative profiles that can be …

Image segmentation is crucial for various research areas. Many computer vision
applications depend on segmenting images to understand the scene, such as autonomous …

Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …