Cells are a fundamental unit of biological organization, and identifying them in imaging data–
cell segmentation–is a critical task for various cellular imaging experiments. While deep …

Deep learning techniques have shown the capability in GNSS reflectometry (GNSS-R) for
retrieving geographical parameters based on GNSS-R observations. Recent studies have …

Data verbalisation is a task of great importance in the current field of natural language
processing, as there is a clear benefit in the transformation of our abundant structured and …

Deep neural networks have been successfully used in the task of black-box modeling of
analog audio effects such as distortion. Improving the processing speed and memory …

Brain metastasis segmentation poses a significant challenge in medical imaging due to the
complex presentation and variability in size and location of metastases. In this study, we first …

The availability of high-quality datasets is crucial for the development of behavior prediction
algorithms in autonomous vehicles. This paper highlights the need for standardizing the use …

In the field of computer vision, self-supervised learning has emerged as a method to extract
robust features from unlabeled data, where models derive labels autonomously from the …

Grasping objects with limited or no prior knowledge about them is a highly relevant skill in
assistive robotics. Still, in this general setting, it has remained an open problem, especially …

The task of anomaly detection is to separate anomalous data from normal data in the
dataset. Models such as deep Convolutional AutoEncoder (CAE) and deep support vector …

Tissue stiffness is related to soft tissue pathologies and can be assessed through palpation
or via clinical imaging systems, eg, ultrasound or magnetic resonance imaging. Typically …