On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study
This work addresses the lack of evidence on large models' diagnostic abilities in biomedical imaging, though it is incremental as it applies existing VLMs to new medical data.
The study evaluated the zero-shot and few-shot robustness of visual language models (VLMs) on medical imaging analysis tasks, demonstrating their effectiveness in analyzing biomedical images like brain MRIs, blood cell microscopy, and chest X-rays.
Recently, large language models (LLMs) have taken the spotlight in natural language processing. Further, integrating LLMs with vision enables the users to explore emergent abilities with multimodal data. Visual language models (VLMs), such as LLaVA, Flamingo, or CLIP, have demonstrated impressive performance on various visio-linguistic tasks. Consequently, there are enormous applications of large models that could be potentially used in the biomedical imaging field. Along that direction, there is a lack of related work to show the ability of large models to diagnose the diseases. In this work, we study the zero-shot and few-shot robustness of VLMs on the medical imaging analysis tasks. Our comprehensive experiments demonstrate the effectiveness of VLMs in analyzing biomedical images such as brain MRIs, microscopic images of blood cells, and chest X-rays.