发表机构
Jönköping University; Halmstad University; Mölnlycke Health Care; Centre for Reliable Machine Learning, Royal Holloway, University of London（延雪平大学; 哈尔姆斯塔德大学; 墨尼克医疗保健公司; 伦敦大学皇家霍洛威学院可靠机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究评估小型多模态语言模型能否通过基于检索的上下文学习为伤口分类提供免训练替代方案，使用两个数据集和多种方法实验，结果显示查询条件下的上下文学习效果较好，Qwen 3.5 27B搭配kNN+MMR表现最佳，该方法可实现适应性伤口图像分类。
AI 中文摘要
伤口图像分类通常被视为特定任务的监督学习问题，标签空间或部署设置变化时需要大量手动标注数据并重新训练。本研究评估小型多模态语言模型能否通过基于检索的上下文学习为伤口分类提供免训练替代方案。实验使用两个公共伤口图像数据集，评估了11个来自不同家族的小型多模态语言模型，通过多种方法进行实验。结果表明查询条件下的上下文学习优于零样本和随机少样本提示。在两个数据集上，Qwen 3.5 27B搭配kNN+MMR取得最佳结果。基于检索的上下文学习允许小型多模态语言模型进行适应性伤口图像分类，紧凑的检索上下文可能支持实际且注重隐私的部署。
英文摘要
Wound image classification is often treated as a task-specific supervised learning problem, requiring substantial amounts of manually labelled data and retraining when the label space or deployment setting changes. This study evaluated whether small multimodal language models (SMLMs) can provide a training-free alternative for wound classification through retrieval-based in-context learning (ICL). Experiments used two public wound-image datasets: the Kaggle wound dataset (1469 images, 10 classes) and the Medetec dataset (560 images, 9 classes). Eleven SMLMs from the Qwen 3.5, Ministral 3, and Gemma 4 families were evaluated under zero-shot prompting and few-shot prompting with random support examples, embedding-based k-nearest-neighbour (kNN) retrieval, and kNN retrieval followed by maximal marginal relevance reranking (MMR). Retrieval-only weighted-kNN controls, support-set reduction experiments, and support-context size sweeps were used to assess the effects of retrieval, model scale, and prompt length. Query-conditioned ICL consistently outperformed zero-shot and random few-shot prompting. On the Kaggle dataset, the best result was achieved by Qwen 3.5 27B with kNN+MMR, reaching 0.872 accuracy and 0.871 F1 score. On Medetec, Qwen 3.5 27B with kNN+MMR reached 0.678 accuracy and 0.670 F1. Larger models exceeded matched weighted-kNN controls, indicating use of retrieved examples beyond nearest-neighbour voting. Retrieval-based ICL degraded modestly under support-set reduction, and most gains saturated with 8-10 support images. Retrieval-based ICL allows SMLMs to perform adaptable wound image classification without task-specific retraining. Compact retrieved contexts may support practical and privacy-conscious deployment, although performance remains dependent on model scale, retrieval strategy, and dataset difficulty.
