FLARE25 Medical Multimodal Dataset
This repository contains a multimodal medical imaging dataset for FLARE 2025 with question-answer pairs across various medical imaging modalities.

Dataset Structure
The dataset is organized into the following main directories:
training/
: Training datavalidation-public/
: Public validation datavalidation-hidden/
: Hidden validation data (answer not released)testing/
: Hidden testing data (not released)
Dataset Statistics
- Total datasets: 19
- Medical imaging modalities: 8
- Task types: 10
- Total images: 50996
- Total questions: 58112
- Data sources: 9
Modalities
The dataset includes the following medical imaging modalities:
Clinical, Dermatology, Endoscopy, Mammography, Microscopy, Retinography, Ultrasound, Xray
Tasks
The dataset supports the following tasks:
Classification, Counting, Detection, Multi-label Classification, Regression, Report_Generation
Dataset Overview
Dataset | Modality | Images | Tasks | Questions | Sources |
---|---|---|---|---|---|
Dermatology_bcn20000 | Dermatology | 12413 | Classification | 3576 | https://doi.org/10.6084/m9.figshare.24140028.v1 |
Xray_IUXRay | Xray | 5908 | Report_Generation | 9742 | https://doi.org/10.1093/jamia/ocv080 |
Ultrasound_iugc | Ultrasound | 5125 | Classification, Detection, Regression | 13302 | https://codalab.lisn.upsaclay.fr/competitions/18413 |
Xray_chestdr | Xray | 4848 | Classification, Multi-label Classification | 4848 | https://doi.org/10.6084/m9.figshare.c.6476047.v1 |
Endoscopy_endo | Endoscopy | 3865 | Classification | 80 | https://doi.org/10.6084/m9.figshare.c.6476047.v1 |
Mammography_CMMD | Mammography | 3582 | Classification | 4493 | https://doi.org/10.7937/tcia.eqde-4b16 |
Xray_periapical | Xray | 2317 | Classification, Multi-label Classification | 4656 | Private |
Clinical_neojaundice | Clinical | 2235 | Classification | 745 | https://doi.org/10.6084/m9.figshare.c.6476047.v1 |
Microscopy_chromosome | Microscopy | 1785 | instance_detection | 1785 | Private |
Retinography_retino | Retinography | 1392 | Classification | 1392 | https://doi.org/10.6084/m9.figshare.c.6476047.v1 |
Microscopy_neurips22cell | Microscopy | 1100 | Counting | 1100 | N/A |
Microscopy_bone_marrow | Microscopy | 1045 | classification | 1045 | PRIVATE |
Xray_boneresorption | Xray | 1004 | regression | 1004 | PRIVATE |
Xray_dental | Xray | 1001 | Classification | 5998 | Private |
Retinography_fundus | Retinography | 987 | Classification | 1974 | Private |
Ultrasound_BUSI | Ultrasound | 780 | classification | 780 | https://doi.org/10.1016/j.dib.2019.104863 |
Ultrasound_BUS-UCLM | Ultrasound | 682 | classification | 682 | https://doi.org/10.1038/s41597-025-04562-3 |
Ultrasound_BUSI-det | Ultrasound | 647 | detection | 647 | https://doi.org/10.1016/j.dib.2019.104863 |
Ultrasound_BUS-UCLM-det | Ultrasound | 263 | detection | 263 | https://doi.org/10.1038/s41597-025-04562-3 |
Note: The numbers shown in the above table include data from all subsets: training, validation-public, validation-hidden, and testing.
Directory Structure
Each dataset typically follows this structure:
modality/
└── dataset_name/
├── images[Tr|Val|Ts]/
│ └── image_files.png
└── dataset_questions_[train|val].json
Question Format
Questions are formatted as JSON arrays with the following structure:
[
{
"TaskType": "Classification",
"Modality": "X-ray",
"ImageName": "imagesTr/image001.png",
"Question": "What abnormality is visible in this image?",
"Answer": "Fracture",
"Split": "train"
}
]
- Downloads last month
- 561
Models trained or fine-tuned on FLARE-MedFM/FLARE-Task5-MLLM-2D
Image-Text-to-Text
•
Updated