The increasing interest in developing Artificial Intelligence applications in the medical domain, suffers from the lack of high-quality dataset, mainly due to privacy-related issues. Moreover, the recent rising of Multimodal Large Language Models (MLLM) leads to a need for multimodal medical datasets, where clinical reports and findings are attached to the corresponding CT or MR scans. This paper illustrates the entire workflow for building the data set MedPix 2.0. Starting from the well-known multimodal dataset MedPix\textsuperscript{\textregistered}, mainly used by physicians, nurses and healthcare students for Continuing Medical Education purposes, a semi-automatic pipeline was developed to extract visual and textual data followed by a manual curing procedure where noisy samples were removed, thus creating a MongoDB database. Along with the dataset, we developed a GUI aimed at navigating efficiently the MongoDB instance, and obtaining the raw data that can be easily used for training and/or fine-tuning MLLMs. To enforce this point, we also propose a CLIP-based model trained on MedPix 2.0 for scan classification tasks.

本文介绍了建立 MedPix 2.0 数据集的整个工作流程，其主要涉及人工智能在医学领域的应用、多模态医学数据集、CT 或 MR 扫描等。同时，还提出了基于 MedPix 2.0 的 CLIP 模型用于扫描分类任务。

MedPix 2.0：一套全面的多模态生物医学数据集用于高级AI应用