Deep learning (DL) is one of the branches of artificial intelligence that has seen exponential
growth in recent years. The scientific community has focused its attention on DL due to its …

Background This paper attempts to conduct a systematic review and meta-analysis of deep
learning (DLs) models for cervical cancer CT image segmentation. Methods Relevant …

Purpose Attenuation correction and scatter compensation (AC/SC) are two main steps
toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI …

We present a deep learning (DL)‐based automated whole lung and COVID‐19 pneumonia
infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography …

Purpose Fetal biometric measurements face a number of challenges, including the presence
of speckle, limited soft-tissue contrast and difficulties in the presence of low amniotic fluid …

Purpose Magnetic resonance (MR) imaging is the gold standard in image‐guided
brachytherapy (IGBT) due to its superior soft‐tissue contrast for target and organs‐at‐risk …

Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of
consensus on how to assess and validate auto-contouring systems currently limits clinical …

In this study, an inter-fraction organ deformation simulation framework for the locally
advanced cervical cancer (LACC), which considers the anatomical flexibility, rigidity, and …

U-Net based algorithms, due to their complex computations, include limitations when they
are used in clinical devices. In this paper, we addressed this problem through a novel U-Net …

Background Emerging data suggest that dose-sparing several key cardiac regions is
prognostically beneficial in lung cancer radiotherapy. The cardiac substructures are …