Clinical diagnosis of the eye is performed over multifarious data modalities including scalar
clinical labels, vectorized biomarkers, two-dimensional fundus images, and three …

This article presents a novel positive and negative set selection strategy for contrastive
learning of medical images based on labels that can be extracted from clinical data. In the …

The capabilities of foundation models most recently the Segment Anything Model have
gathered a large degree of attention for providing a versatile framework for tackling a wide …

Recently, convolutional neural networks (CNNs) have been widely applied in the seismic
facies classification. However, even state-of-the-art CNN architectures often encounter …

In this work, we present a methodology to shape a fisheye-specific representation space that
reflects the interaction between distortion and semantic context present in this data modality …

Forecasting human trajectories in traffic scenes is critical for safety within mixed or fully
autonomous systems. Human future trajectories are driven by two major stimuli, social …

In this work, we propose a novel supervised contrastive loss that enables the integration of
taxonomic hierarchy information during the representation learning process. A supervised …

In this work we explore deep metric learning as a data-driven approach for segmentation of
first-order stratigraphic units in 3D seismic, based on similarities in reflection patterns. We …

With the advent of machine learning (ML) and deep learning in geophysics interpretation
tasks, ML models especially classifiers have proven valuable in assessing hydrocarbon …

Recently, seismic facies classification based on convolutional neural networks (CNN) has
garnered significant research interest. However, existing CNN-based supervised learning …