Medical image segmentation is one of the hot issues in the related area of image
processing. Precise segmentation for medical images is a vital guarantee for follow‐up …

Background and objective: Medical image segmentation plays an important role in clinic.
Recently, with the development of deep learning, many convolutional neural network (CNN) …

The performance of a Convolutional Neural Network (CNN) highly depends on its
architecture and corresponding parameters. Manually designing a CNN is a time-consuming …

One of the most important areas in medical image analysis is segmentation, in which raw
image data is partitioned into structured and meaningful regions to gain further insights. By …

Adapting an existing convolutional neural network architecture to a specific dataset for
medical image segmentation remains a challenging task that requires extensive expertise …

Pre‐segmentation is known as a crucial step in medical image analysis. Many approaches
have been proposed to make improvement to both the quality and efficiency of …

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

Accurate segmentation of medical images is essential for clinical decision-making, and deep
learning techniques have shown remarkable results in this area. However, existing …

Accurate segmentation of lesions in medical images is of great significance for clinical
diagnosis and evaluation. The low contrast between lesions and surrounding tissues …

Abstract Background and Objective: Segmentation is a key step in biomedical image
analysis tasks. Recently, convolutional neural networks (CNNs) have been increasingly …