Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to
develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to …

Abstract Multiple Instance Learning (MIL) is a learning paradigm, where training instances
are arranged in sets, called bags, and only bag-level labels are available during training …

Purpose: To evaluate a novel deep learning algorithm to distinguish between eyes that may
or may not have a graft detachment based on pre–Descemet membrane endothelial …

In the realm of medical diagnostics, the utilization of deep learning techniques, notably in the
context of radiology images, has emerged as a transformative force. The significance of …

In the case of specific immunotherapy regimens and access to pre-treatment CT scans,
developing reliable, interpretable intelligent image biomarkers to predict efficacy is essential …

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Accurate segmentation of the
left atrial (LA) and LA scars can provide valuable information to predict treatment outcomes …

Artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML) have
revolutionized disease diagnosis using complex medical images such as X-rays and CT …

COVID-19 analysis from medical imaging is an important task that has been intensively
studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical …

Objective. Honeycomb lung is a rare but severe disease characterized by honeycomb-like
imaging features and distinct radiological characteristics. Therefore, this study aims to …

Retinal vessels play a pivotal role as biomarkers in the detection of retinal diseases,
including hypertensive retinopathy. The manual identification of these retinal vessels is both …