MedPix 2.0: A Comprehensive Multimodal Biomedical Data set for Advanced AI Applications

The increasing interest in developing Artificial Intelligence applications in the medical domain, suffers from the lack of high-quality data set, mainly due to privacy-related issues. Moreover, the recent rising of Large Multimodal Models (LMM) leads to a need for multimodal medical data sets, 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 data set MedPix, 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 data set, 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 LMMs. To enforce this point, we also propose a CLIP-based model trained on MedPix 2.0 for scanning modality and location classification tasks. MedPix 2.0 is available on GitHub

@article{siragusa2025_2407.02994,
  title={ MedPix 2.0: A Comprehensive Multimodal Biomedical Data set for Advanced AI Applications },
  author={ Irene Siragusa and Salvatore Contino and Massimo La Ciura and Rosario Alicata and Roberto Pirrone },
  journal={arXiv preprint arXiv:2407.02994},
  year={ 2025 }
}