Deep learning has remarkably impacted several different scientific disciplines over the last
few years. For example, in image processing and analysis, deep learning algorithms were …

With the massive proliferation of data-driven algorithms, such as deep learning-based
approaches, the availability of high-quality data is of great interest. Volumetric data is very …

This paper presents a deep-learning surrogate model tailored for a fast generation of
realistic ultrasonic images in the Multi-modal Total Focusing Method (M-TFM) framework …

Data has become the most valuable resource in today's world. With the massive proliferation
of data-driven algorithms, such as deep learning-based approaches, the availability of data …

Motion artifacts in magnetic resonance imaging (MRI) have always been a serious issue
because they can affect subsequent diagnosis and treatment. Supervised deep learning …

Glottis segmentation is a crucial step to quantify endoscopic footage in laryngeal high-speed
videoendoscopy. Recent advances in deep neural networks for glottis segmentation allow …

This paper explores self-supervised disentangled representation learning within sequential
data, focusing on separating time-indep-endent and time-varying factors in videos. We …

Abstract A robust PET-CT segmentation network should guarantee that models trained on
the PET-CT images will still work when only CT images are available. It is particularly …

Despite the remarkable advances in deep learning for medical image analysis, it has
become evident that biases in datasets used for training such models pose considerable …

The reconstruction loss and the Kullback-Leibler divergence (KLD) loss in a variational
autoencoder (VAE) often play antagonistic roles, and tuning the weight of the KLD loss in β …