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

TL;DR

This paper demonstrates that large vision-language models like GPT-4V can effectively perform medical image classification tasks in histopathology through in-context learning, reducing the need for extensive labeled datasets and specialized training.

Contribution

It shows that GPT-4V can be used out-of-the-box for cancer histopathology classification tasks via in-context learning, outperforming some specialized models with minimal samples.

Findings

In-context learning matches or surpasses specialized neural networks.

GPT-4V requires few samples for effective classification.

Medical image analysis can leverage generalist AI models.

Abstract

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. Here, we systematically evaluate the model Generative Pretrained Transformer 4 with Vision capabilities (GPT-4V) on cancer image processing with in-context learning on three cancer histopathology tasks of high importance: Classification of tissue subtypes in colorectal cancer, colon polyp subtyping and breast tumor detection in lymph node sections. Our results show that in-context learning is sufficient to match or even outperform specialized neural networks trained for particular tasks, while only requiring a minimal number of samples. In summary, this study demonstrates that large vision language models trained on non-domain specific data can be applied out-of-the box to solve medical image-processing tasks in histopathology. This democratizes access of generalist AI models to medical experts without technical background especially for areas where annotated data is scarce.