Medical image classification remains a challenging process in deep learning. Here, the authors evaluate a large vision language foundation model (GPT-4V) with in-context learning for cancer image processing and show that such models can learn from examples and reach performance similar to specialized neural networks while reducing the gap to current state-of-the art pathology foundation models.
- Dyke Ferber
- Georg Wölflein
- Jakob Nikolas Kather
