Few-shot learning has been studied to adapt models to tasks with very few samples. It holds profound significance, particularly in clinical tasks, due to the high annotation cost of medical images. Several works have explored few-shot learning on medical images, yet they still require a large number of medical images for pre-training models to gain domain-specific priors. Vision foundation models recently have achieved remarkable success in natural images. Hence, adapting rapidly advancing vision foundation models from natural images to few-shot clinical tasks holds great promise. MedFMC has recently organized a challenge to shed more light on this topic at NeurIPS 2023. In this work, we present our challenge solution. We observe that a simple variant of fine-tuning with partial freezing shows remarkable performance. Empirical evidence demonstrates that this approach could outperform various common fine-tuning methods under limited sample sizes. Additionally, we explore enhanced utilization of semantic supervision to boost performance. We propose a novel approach that contextualizes labels via large language models (LLMs). Our findings reveal that the context generated by LLMs significantly enhances the discrimination of semantic embeddings for similar categories, resulting in a notable performance improvement of 3%-5% in 1-shot settings compared to commonly employed one-hot labels and other semantic supervision methods. Our solution secures the 1st place in the MedFMC challenge.

翻译：小样本学习旨在研究如何利用极少样本将模型适配至特定任务。由于医学图像标注成本高昂，该方法在临床任务中具有深远意义。已有研究探索了医学图像的小样本学习，但仍需大量医学图像进行预训练以获取领域先验知识。近年来，视觉基础模型在自然图像领域取得了显著成功，因此将快速发展的视觉基础模型从自然图像适配至小样本临床任务具有巨大潜力。MedFMC近期在NeurIPS 2023上组织了相关挑战赛以深化该领域研究。本文呈现了我们的解决方案：观察到部分冻结参数的简单微调变体展现出卓越性能。实验证据表明，在样本量有限条件下，该方法可超越多种常见微调策略。此外，我们探索了语义监督的强化应用以提升性能，提出一种通过大语言模型（LLMs）对标签进行情境化处理的新方法。研究发现，LLMs生成的语义上下文能显著增强相似类别语义嵌入的区分度，在1-shot设置下相比常用独热编码及其他语义监督方法实现了3%-5%的性能提升。本方案最终在MedFMC挑战赛中荣获第一名。