VisionCLIP: An Med-AIGC based Ethical Language-Image Foundation Model
  for Generalizable Retina Image Analysis
Hao Wei Bowen Liu Minqing Zhang Peilun Shi Wu Yuan
Generalist foundation model has ushered in newfound capabilities in medical
domain. However, the contradiction between the growing demand for high-quality
annotated data with patient privacy continues to intensify. The utilization of
medical artificial intelligence generated content (Med-AIGC) as an
inexhaustible resource repository arises as a potential solution to address the
aforementioned challenge. Here we harness 1 million open-source synthetic
fundus images paired with natural language descriptions, to curate an ethical
language-image foundation model for retina image analysis named VisionCLIP.
VisionCLIP achieves competitive performance on three external datasets compared
with the existing method pre-trained on real-world data in a zero-shot fashion.
The employment of artificially synthetic images alongside corresponding textual
data for training enables the medical foundation model to successfully
assimilate knowledge of disease symptomatology, thereby circumventing potential
breaches of patient confidentiality.