BACKGROUND: Skin cancer (SC) is a global health concern because of its high and still increasing incidence and associated health care cost. Belgium is no exception as 1 in 5 people are diagnosed with SC before the age of 75 years. The VECTRA WB360, a...
Variable physiological [F]FDG uptake patterns and a lack of labelled data make it challenging to automatically distinguish normal from pathological suspicious uptake in whole-body PET/CT imaging. We propose a deep learning method that generates patie...
. Quantitative assessment of treatment response in advanced prostate cancer (APC) with bone metastases remains an unmet clinical need. Whole-body diffusion-weighted MRI (WB-DWI) provides two response biomarkers: total diffusion volume (TDV) and globa...
The integration of deep learning in medical imaging has significantly advanced diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to inherent inter-modality variabilit...
The lengthy 1 h dynamic positron emission tomography (PET) scans discomfort patients, add motion artifacts, and inflate costs, highlighting the need for tech advancements to reduce scan times. Therefore, we attempted to reconstruct multi-parametric i...
This study aims to enhance the dosimetry accuracy in I planar imaging by utilizing a single oblique view and Monte Carlo (MC) validated dose point kernels (DPKs) alongside the integration of artificial intelligence (AI) for accurate dose prediction w...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 18, 2025
Medical imaging models for object identification often rely on extensive pretraining data, which is difficult to obtain due to data scarcity and privacy constraints. In practice, hospitals typically have access only to pretrained model weights withou...
OBJECTIVES: To develop a deep neural network for automatic bowel segmentation and assess its applicability for estimating large bowel length (LBL) in individuals with constipation.
BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3DF-fluorodeoxyglucose (FDG)-Position emission tomography (PET) with computed tomography (CT) images agnostic to disease location and site.
IEEE transactions on pattern analysis and machine intelligence
Mar 6, 2025
Recovering whole-body mesh by inferring the abstract pose and shape parameters from visual content can obtain 3D bodies with realistic structures. However, the inferring process is highly non-linear and suffers from image-mesh misalignment, resulting...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.