标题： M3Retrieve：医学多模态检索基准测试 显示英文标题

标题： M3Retrieve: Benchmarking Multimodal Retrieval for Medicine

摘要： 随着检索增强生成（RAG）的日益广泛应用，强大的检索模型比以往任何时候都更加重要。在医疗领域，结合文本和图像信息的多模态检索模型在许多下游任务中具有重大优势，例如问答、跨模态检索和多模态摘要，因为医疗数据通常包含这两种格式。然而，目前尚无标准基准来评估这些模型在医疗环境中的表现。为解决这一差距，我们引入了M3Retrieve，这是一个多模态医学检索基准。M3Retrieve涵盖5个领域、16个医学领域和4项不同的任务，包含超过120万篇文本文档和16.4万个多模态查询，所有数据均在获得批准的许可下收集。我们在该基准上评估领先的多模态检索模型，以探索不同医学专科特有的挑战，并了解它们对检索性能的影响。通过发布M3Retrieve，我们的目标是实现系统评估，促进模型创新，并加速构建更强大且可靠的多模态检索系统用于医疗应用。该数据集和基线代码可在以下github页面获取https://github.com/AkashGhosh/M3Retrieve. 显示英文摘要

摘要： With the increasing use of RetrievalAugmented Generation (RAG), strong retrieval models have become more important than ever. In healthcare, multimodal retrieval models that combine information from both text and images offer major advantages for many downstream tasks such as question answering, cross-modal retrieval, and multimodal summarization, since medical data often includes both formats. However, there is currently no standard benchmark to evaluate how well these models perform in medical settings. To address this gap, we introduce M3Retrieve, a Multimodal Medical Retrieval Benchmark. M3Retrieve, spans 5 domains,16 medical fields, and 4 distinct tasks, with over 1.2 Million text documents and 164K multimodal queries, all collected under approved licenses. We evaluate leading multimodal retrieval models on this benchmark to explore the challenges specific to different medical specialities and to understand their impact on retrieval performance. By releasing M3Retrieve, we aim to enable systematic evaluation, foster model innovation, and accelerate research toward building more capable and reliable multimodal retrieval systems for medical applications. The dataset and the baselines code are available in this github page https://github.com/AkashGhosh/M3Retrieve.