In the last decade, research on artificial intelligence has seen rapid growth with deep
learning models, especially in the field of medical image segmentation. Various studies …

Deep learning has been extensively applied to segmentation in medical imaging. U‐Net
proposed in 2015 shows the advantages of accurate segmentation of small targets and its …

Image segmentation is a branch of digital image processing which has numerous
applications in the field of analysis of images, augmented reality, machine vision, and many …

Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …

Producing quality segmentation masks for images is a fundamental problem in computer
vision. Recent research has explored large-scale supervised training to enable zero-shot …

In this work, we address a realistic case of unsupervised domain adaptation, where the
source label set subsumes that of the target. This relaxation in the requirement of an …

Image segmentation, a crucial process of dividing images into distinct parts or objects, has
witnessed remarkable advancements with the emergence of deep learning (DL) techniques …

Identifying the tumour's extent is a major challenge in planning treatment for brain tumours
and correctly measuring their size. Magnetic resonance imaging (MRI) has emerged as a …

Dermoscopy images can be classified more accurately if skin lesions or nodules are
segmented. Because of their fuzzy borders, irregular boundaries, inter-and intra-class …

Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …