Abstract Large Vision-Language Models (LVLMs) have demonstrated remarkable
capabilities in various multimodal tasks. However their potential in the medical domain …

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it
combines information from various imaging modalities to provide a more comprehensive …

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 …

Data is the cornerstone of using AI models, because their performance directly depends on
the diversity, quantity, and quality of the data used for training. Using AI presents unique …

Multi-modal ophthalmic image classification plays a key role in diagnosing eye diseases, as
it integrates information from different sources to complement their respective performances …

Humans exhibit disagreement during data labeling. We term this disagreement as human
label uncertainty. In this work, we study the ramifications of human label uncertainty (HLU) …

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 …

This paper conjectures and validates a framework that allows for action during inference in
supervised neural networks. Supervised neural networks are constructed with the objective …

The automatic classification of 3D medical data is memory-intensive. Also, variations in the
number of slices between samples is common. Naïve solutions such as subsampling can …

This paper 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 …