Cost-effective instruction learning for pathology vision and language analysis

Kaitao Chen, Mianxin Liu, Fang Yan, Lei Ma, Xiaoming Shi, Lilong Wang, Xiaosong Wang, Lifeng Zhu, Zhe Wang, Mu Zhou, Shaoting Zhang

Abstract: The advent of vision–language models fosters interactive conversations between artificial intelligence-enabled models and humans. However, applying these models in the clinic faces challenges related to large-scale training data as well as financial and computational resources. Here we propose CLOVER, a cost-effective instruction learning framework for conversational pathology. CLOVER trains a lightweight module and uses instruction tuning while freezing the parameters of the large language model. Instead of using costly GPT-4, we propose well-designed prompts on GPT-3.5 for building generation-based instructions, emphasizing the utility of pathological knowledge derived from the Internet source. We construct a high-quality set of template-based instructions in the context of digital pathology. Using two benchmark datasets, our findings reveal the strength of hybrid-form, pathological visual question–answer instructions. CLOVER outperforms baselines that possess 37 times more training parameters and exhibits few-shot capacity on an external clinical dataset. CLOVER could thus accelerate the adoption of rapid conversational applications in digital pathology. Training foundation models often requires a costly budget and excessive computational resources. In this study, a low-cost instruction learning framework is proposed that could enable the rapid adoption of visual-language pathology applications.

External IDs:dblp:journals/ncs/ChenLYMSWWZWZZ25