Cost-effective instruction learning for pathology vision and language analysis.

Area of Science:

• Artificial Intelligence
• Medical Informatics
• Digital Pathology

Background:

• Vision-language models (VLMs) enable human-AI interaction but face challenges in clinical settings due to data, financial, and computational demands.
• Current VLMs require substantial resources, limiting their widespread application in specialized fields like pathology.

Purpose of the Study:

• To introduce CLOVER, a cost-effective instruction learning framework for conversational AI in digital pathology.
• To address the resource limitations of existing VLMs by proposing an efficient training methodology.

Main Methods:

• CLOVER employs instruction tuning on a lightweight module while freezing the parameters of a large language model.
• It utilizes well-designed prompts on GPT-3.5, leveraging internet-sourced pathological knowledge for instruction generation.
• Template-based instructions specific to digital pathology were developed and utilized.

Main Results:

• CLOVER demonstrated the effectiveness of hybrid-form, pathological visual question-answer instructions.
• The framework significantly outperformed baseline models with substantially more training parameters (37x).
• CLOVER exhibited few-shot learning capabilities on an external clinical dataset.

Conclusions:

• CLOVER presents a viable and resource-efficient solution for developing conversational AI in digital pathology.
• The framework has the potential to accelerate the integration of rapid conversational applications within clinical workflows.
• This approach highlights the utility of leveraging readily available knowledge sources and efficient model training for specialized AI applications.