Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of
metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative …

The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …

Over the past 2 decades, the field of hepatology has witnessed major developments in
diagnostic tools, prognostic models, and treatment options making it one of the most …

Hematoxylin and eosin stained whole slide images (WSIs) are the gold standard for
pathologists and medical professionals for tumor diagnosis, surgery planning, and …

In common medical procedures, the time-consuming and expensive nature of obtaining test
results plagues doctors and patients. Digital pathology research allows using computational …

Diagnostic disagreements among pathologists occur throughout the spectrum of benign to
malignant lesions. A computer-aided diagnostic system capable of reducing uncertainties …

Background: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a
wide range of applications in image analysis, ranging from automated segmentation to …

Simple Summary The use of neoadjuvant therapy (NAT) in patients with pancreatic ductal
adenocarcinoma (PDAC) is increasing. Objective quantification of the histopathological …

Medical Imaging has become a vital technique that has been embraced in the diagnosis and
treatment process of cancer. Histopathological slides, which microscopically examine the …

Recent advancements in biomedical image analysis have been significantly driven by the
Segment Anything Model (SAM). This transformative technology, originally developed for …