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

One critical challenge in deploying highly performant machine learning models in real-life
applications is out of distribution (OOD) detection. Given a predictive model which is …

In this paper, we visualize and quantify the predictive uncertainty of gradient-based post hoc
visual explanations for neural networks. Predictive uncertainty refers to the variability in the …

Understanding the severity of conditions shown in images in medical diagnosis is crucial
serving as a key guide for clinical assessment treatment as well as evaluating longitudinal …

This paper presents a novel approach to active learning that takes into account the non-
independent and identically distributed (non-iid) structure of a clinical trial setting. There …

Conventional machine learning systems that operate on natural images assume the
presence of attributes within the images that lead to some decision. However, decisions in …

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 …

The widespread adoption of deep neural networks in machine learning calls for an objective
quantification of esoteric trust. In this paper we propose GradTrust, a classification trust …