Radiomics is a technique that extracts quantitative features from medical images using data‐
characterization algorithms. Radiomic features can be used to identify tissue characteristics …

Quantitative extraction of imaging features from medical scans ('radiomics') has attracted a
lot of research attention in the last few years. The literature has consistently emphasized the …

Objectives Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical
during the epidemic. We aim to identify differences in CT imaging and clinical manifestations …

In view of the low diagnostic accuracy of the current classification methods of benign and
malignant pulmonary nodules, this paper proposes a 3D segmentation attention network …

Abstract Purpose Low-Dose Computed Tomography (LDCT) is the most common imaging
modality for lung cancer diagnosis. The presence of nodules in the scans does not …

Digitization of medicine requires systematic handling of the increasing amount of health data
to improve medical diagnosis. In this context, the integration of the versatile diagnostic …

Background Accurate segmentation of pulmonary nodules on computed tomography (CT)
scans plays a crucial role in the evaluation and management of patients with suspicion of …

Objectives Pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP)
are diseases with a highly analogous visual presentation that are difficult to distinguish by …

In this paper, we investigate the role of shape and texture features from 18 F-FDG PET/CT to
discriminate between benign and malignant solitary pulmonary nodules. To this end, we …

Purpose Lung cancer represents the first cause of cancer-related death in the world.
Radiomics studies arise rapidly in this late decade. The aim of this review is to identify …