Medical tasks are prone to inter-rater variability due to multiple factors such as image quality,
professional experience and training, or guideline clarity. Training deep learning networks …

Background Outlining acutely infarcted tissue on non-contrast CT is a challenging task for
which human inter-reader agreement is limited. We explored two different methods for …

Manual annotation of medical images is highly subjective, leading to inevitable annotation
biases. Deep learning models may surpass human performance on a variety of tasks, but …

Abstract We introduce AiD Regen, a novel system that generates 3D wound models
combining 2D semantic segmentation with 3D reconstruction so that they can be printed via …

Recent developments in deep learning (DL) techniques have led to great performance
improvement in medical image segmentation tasks, especially with the latest Transformer …

Many automatic machine learning models developed for focal pathology (eg lesions,
tumours) detection and segmentation perform well, but do not generalize as well to new …

In recent years, supervised deep learning networks have achieved state-of-the-art results in
many public medical segmentation challenges. In spite of their success on isolated datasets …

To date, several automated strategies for identification/segmentation of Multiple Sclerosis
(MS) lesions with the use of Magnetic Resonance Imaging (MRI) have been presented, but …

This paper investigates the numerical uncertainty of Convolutional Neural Networks (CNNs)
inference for structural brain MRI analysis. It applies Random Rounding a stochastic …

Deep learning-based medical image segmentation has revolutionized healthcare
diagnostics. While the accuracy offered by these models is important, ensuring their practical …