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.

通过部分冻结的简单微调变体和上下文化标签，提出了一种在有限样本大小下超越传统微调方法的方法，从而显著提高了医学图像的语义嵌入区分度，使得在 1-shot 设置下与常用的 one-hot 标签和其他语义监督方法相比性能提高了 3%-5%。

基于大型语言模型的高效少样本临床任务适应

Efficient Few-Shot Clinical Task Adaptation with Large Language Models

Compositional vector space models of meaning promise new solutions to
stubborn language understanding problems. This paper makes two contributions
toward this end: (i) it uses automatically-extracted paraphrase examples as a
source of supervision for training compositional models, replacing previous
work which relied on manual annotations used for the same purpose, and (ii)
develops a context-aware model for scoring phrasal compositionality.
Experimental results indicate that these multiple sources of information can be
used to learn partial semantic supervision that matches previous techniques in
intrinsic evaluation tasks. Our approaches are also evaluated for their impact
on a machine translation system where we show improvements in translation
quality, demonstrating that compositionality in interpretation correlates with
compositionality in translation.

本文提出了用自动提取的近义例子替代手工注释进行组合模型训练的方法，同时开发了一种上下文感知的短语组成能力评分模型，实验结果表明这些方法可以用于提高机器翻译系统的翻译质量。