Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning

Most advances in medical image recognition supporting clinical auxiliary diagnosis meet challenges due to the low-resource situation in the medical field, where annotations are highly expensive and professional. This low-resource problem can be alleviated by leveraging the transferable representations of large-scale pre-trained vision-language models via relevant medical text prompts. However, existing pre-trained vision-language models require domain experts to carefully design the medical prompts, which greatly increases the burden on clinicians. To address this problem, we propose a weakly supervised prompt …

advances annotations challenges classification clinical cs.cv diagnosis image image recognition language language models low medical medical field professional prompt prompt learning prompts recognition scale text via vision vision-language models