Few-shot in-context prompting improves frozen vision-language model F1 scores on three cancer image datasets, with GPT-4o reaching 0.81 binary and 0.60 multi-class.
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In-Context Learning for Label-Efficient Cancer Image Classification in Oncology
Few-shot in-context prompting improves frozen vision-language model F1 scores on three cancer image datasets, with GPT-4o reaching 0.81 binary and 0.60 multi-class.