Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the
mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human …

The increasing use of computed tomography (CT) and cone beam computed tomography
(CBCT) in oral and maxillofacial imaging has driven the development of deep learning and …

Background Numerous features are commonly generated in radiomics applications as
applied to medical imaging, and identification of robust radiomics features (RFs) can be an …

Background: Although handcrafted radiomics features (RF) are commonly extracted via
radiomics software, employing deep features (DF) extracted from deep learning (DL) …

Objectives To train and validate a cloud-based convolutional neural network (CNN) model
for automated segmentation (AS) of dental implant and attached prosthetic crown on cone …

Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient
information directly from images that are decoded into handcrafted features, comprising …

Objective Development and validation of a radiomics nomogram for predicting recurrence
and adjuvant therapy benefit populations in high/intermediate-risk gastrointestinal stromal …

In this section, a various levelled picture matting method is utilized to extract veins from
fundus pictures. All the more explicitly, a various levelled methodology is joined into the …

Purpose The purpose of this guideline is to provide comprehensive information on best
practices for robust radiomics analyses for both hand-crafted and deep learning-based …

Pain is an unpleasant sensory and emotional experience. Understanding the neural
mechanisms of acute and chronic pain and the brain changes affecting pain factors is …