Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …

Foundation models (FMs) are large-scale deeplearning models that are developed using
large datasets and self-supervised learning methods. These models serve as a base for …

We introduce eCLIP, an enhanced version of the CLIP model that integrates expert
annotations in the form of radiologist eye-gaze heatmaps. It tackles key challenges in …

Fairness is a critical concern in deep learning especially in healthcare where these models
influence diagnoses and treatment decisions. Although fairness has been investigated in the …

Multimodal foundation models hold significant potential for automating radiology report
generation, thereby assisting clinicians in diagnosing cardiac diseases. However, generated …

Multi-modality learning, exemplified by the language-image pair pre-trained CLIP model,
has demonstrated remarkable performance in enhancing zero-shot capabilities and has …

Medical Vision Language Pretraining (VLP) has recently emerged as a promising solution to
the scarcity of labeled data in the medical domain. By leveraging paired/unpaired vision and …

The increasing demand for medical imaging has surpassed the capacity of available
radiologists, leading to diagnostic delays and potential misdiagnoses. Artificial intelligence …

Foundation model, which is pre-trained on broad data and is able to adapt to a wide range
of tasks, is advancing healthcare. It promotes the development of healthcare artificial …

Current vision-language pre-training (VLP) methodologies predominantly depend on paired
image-text datasets a resource that is challenging to acquire in radiology due to privacy …