The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …

Medical Imaging (MI) plays a crucial role in healthcare, including disease diagnosis,
treatment, and continuous monitoring. The integration of non-invasive techniques such as X …

Medical image segmentation is an essential task in clinical diagnosis and case analysis.
Most of the existing methods are based on U-shaped convolutional neural networks (CNNs) …

We investigate a fundamental aspect of machine vision: the measurement of features by
revisiting clustering one of the most classic approaches in machine learning and data …

Automatic diagnosing lung cancer from computed tomography scans involves two steps:
detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary …

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning
(DL), are widely used for various computer vision tasks such as image classification, object …

Learning on data represented with multiple views (eg, multiple types of descriptors or
modalities) is a rapidly growing direction in machine learning and computer vision. Although …

Lung cancer is the most common cause of cancer death worldwide. A timely diagnosis of the
pulmonary nodules makes it possible to detect lung cancer in the early stage, and thoracic …

Accurate extraction of cracks is important yet challenging in bridge inspection, particularly
that of tiny cracks captured from high-resolution (HR) images. This paper presents a crack …

Over the last years, fractal and multifractal geometries were applied extensively in many
medical signal (1D, 2D or 3D) analysis applications like pattern recognition, texture analysis …