Deep learning using convolutional neural networks is an actively emerging field in
histological image analysis. This study explores deep learning methods for computer-aided …

Highlights • Metal nanoparticles have been extensively explored for the management of
various disease conditions including cancer. • Metal nanoparticles showed inherent anticancer …

Self-supervised learning in vision--language processing (VLP) exploits semantic alignment
between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the …

The aim of the study was to prepare chemotherapeutic agent-loaded zinc oxide nanoparticles
for the intracellular delivery of drug, for better therapeutic activity. Zinc oxide nanoparticles …

Background During the unfortunate event of the COVID-19 pandemic, restrictions were placed
on face-to-face interactions in dentistry to promote social distancing and reduce spread of …

Mucoadhesive drug delivery systems came into picture in the early 1980s and are one of
the most studied novel delivery systems. Several researchers have focused on the …

Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent.
Long training period, insufficient recruitment and poor retention of sonographers are …

This paper describes a multi-resolution technique to combine diagnostically important visual
information at different magnifications in H&E whole slide images (WSI) of gastric cancer. …

Digital image analysis of histological datasets is a currently expanding field of research.
With different stains, magnifications and types of tissues, histological images are inherently …

The recent success of general-domain large language models (LLMs) has significantly
changed the natural language processing paradigm towards a unified foundation model across …