Vision-language pre-training for chest X-rays has made significant strides, primarily by
utilizing paired radiographs and radiology reports. However, existing approaches often face …

In the field of medical Vision-Language Pretraining (VLP), significant efforts have been
devoted to deriving text and image features from both clinical reports and associated …

Vision-and-language pretraining (VLP) in the medical field utilizes contrastive learning on
image-text pairs to achieve effective transfer across tasks. Yet, current VLP approaches with …

Medical vision-language pre-training has emerged as a promising approach for learning
domain-general representations of medical image and text. Current algorithms that exploit …

The large-scale pre-trained vision language models (VLM) have shown remarkable domain
transfer capability on natural images. However, it remains unknown whether this capability …

While multi-modal foundation models pre-trained on large-scale data have been successful
in natural language understanding and vision recognition, their use in medical domains is …

Modern healthcare often utilises radiographic images alongside textual reports for
diagnostics, encouraging the use of Vision-Language Self-Supervised Learning (VL-SSL) …

Medical Vision-Language Pre-training (VLP) learns representations jointly from medical
images and paired radiology reports. It typically requires large-scale paired image-text …

Medical vision-language models enable co-learning and integrating features from medical
imaging and clinical text. However, these models are not easy to train and the latent …

Owing to a large amount of multi-modal data in modern medical systems, such as medical
images and reports, Medical Vision-Language Pre-training (Med-VLP) has demonstrated …