In-context learning enables multimodal large language models to classify cancer pathology images

Understand

Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models.

• However, this process is computationally and technically demanding.
• In language processing, in-context learning provides an alternative, where models learn from within prompts, bypassing the need for parameter updates.
• Yet, in-context learning remains underexplored in medical image analysis.