Purpose We recently described the validation of deep learning-based auto-segmented
contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study …

Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic
procedures for early detection and diagnosis are crucial. Some difficulties with manual …

In the domain of using DL-based methods in medical and healthcare prediction systems, the
utilization of state-of-the-art deep learning (DL) methodologies assumes paramount …

Medical image segmentation is an important tool for current clinical applications. It is the
backbone of numerous clinical diagnosis methods, oncological treatments and computer …

Deep learning (DL) has become widely used for medical image segmentation in recent
years. However, despite these advances, there are still problems for which DL-based …

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …

Purpose Noticing the fast growing translation of artificial intelligence (AI) technologies to
medical image analysis this paper emphasizes the future role of the medical physicist in this …

Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …

Purpose Radiomics are a set of methods used to leverage medical imaging and extract
quantitative features that can characterize a patient's phenotype. All modalities can be used …

In The Lancet Digital Health, Bin Lou and colleagues1 apply deep learning methods to
analyse pre-treatment CT scans in a retrospective cohort study of 944 patients (849 in the …