Uncertainty estimation in healthcare involves quantifying and understanding the inherent
uncertainty or variability associated with medical predictions, diagnoses, and treatment …

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

Abstract Machine learning (ML) models have become a key component in modern world
services. In decision-making domains where human expertise is crucial, for example, for …

The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …

Artificial intelligence (AI) methods applied to healthcare problems have shown enormous
potential to alleviate the burden of health services worldwide and to improve the accuracy …

High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that
typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is …

X-ray computed tomography (XCT) has been shown to be a reliable tool for quality
inspection, material evaluation, and dimensional measurement tasks across diverse …

Artificial intelligence (AI), the process of training a computer to make data-driven decision on
its own, has shown promising results in various areas of oncology. While several AI tools for …

Purpose Deep convolutional neural networks (CNN) are promising for automatic
classification of dopamine transporter (DAT)-SPECT images. Reporting the certainty of CNN …

Investigating methods for the detection of internal voids within road structures is a critical
measure to ensure the safety and integrity of roadway operations. The purpose of this …