Deep neural networks have achieved great success in medical image segmentation
problems such as liver, kidney, the accuracy of which already exceeds the human level …

The objective of this study is to perform a comprehensive bibliometric analysis of the existing
literature on breast cancer segmentation using deep learning techniques. Data for this …

The encoder-decoder model is a commonly used Deep learning (DL) model for medical
image segmentation. Encoder-decoder models make pixel-wise predictions that focus …

In a class of piecewise-constant image segmentation models, we propose to incorporate a
weighted difference of anisotropic and isotropic total variation (AITV) to regularize the …

Graph total variation methods have been proved to be powerful tools for unstructured data
classification. The existing algorithms, such as MBO (short for M erriman, B ence, and O …

In order to exploit effectively the benefits of classical variational methods with good
interpretability and high generalization performance, this paper proposes a novel …

Although new coating development for improved surface protection is necessary, its manual
application has been a difficult problem to solve. In this study, a convolution neural network …

Image segmentation means to partition an image into separate meaningful regions.
Segmentation in medical images can extract different organs, lesions, and other regions of …

Breast ultrasound segmentation is a challenging task in practice due to speckle noise, low
contrast and blurry boundaries. Although numerous methods have been developed to solve …

Graph Neural Networks (GNNs) are powerful deep learning models designed for graph-
structured data, demonstrating effectiveness across a wide range of applications. The …