Medical Imaging Multimodal Dataset for Disease Detection and Diagnosis
This dataset includes several collections of medical images covering chest radiography, chest CT, brain MRI, retinal and digestive imaging. It allows advanced research in AI-assisted diagnosis, including the detection of cancers, lung diseases, retinal and gastrointestinal disorders.
Approximately 1.1 GB, JPEG/PNG images, more than 7,000 images divided into 5 sub-datasets (CT, X-rays, MRI, retinal, GI tract)
Attribution 4.0 International (CC BY 4.0)
Description
The Medical Imaging Multimodal Dataset is a diverse collection of annotated medical images, including lung CT scans, chest X-rays, brain MRIs, retinal images, and images of the gastrointestinal tract. Each sub-dataset is labeled for different diagnoses such as lung cancers, COVID-19, brain tumors, diabetic retinopathies, and digestive pathologies. The quality and diversity of images make them an excellent support for the research and development of machine learning models in medical imaging.
What is this dataset for?
- Develop and validate ML models for the automated diagnosis of medical diseases.
- Comparing different imaging modalities for multimodal detection research.
- To form models capable of distinguishing various types of pathologies in several clinical areas.
Can it be enriched or improved?
This dataset can be enriched by adding more detailed clinical annotations, additional images from other modalities or institutions, or by developing standardization protocols to facilitate multimodal integration. Additional manual annotation and image segmentation are possible areas for improvement.
🔎 In summary
🧠 Recommended for
- Medical AI researchers
- Diagnostic Solution Developers
- Imaging specialists
🔧 Compatible tools
- PyTorch
- TensorFlow
- MONAI
- ITK-SNAP
- 3D Slicer
💡 Tip
Leverage multimodal diversity to create models that are robust to multiple types of medical images.
Frequently Asked Questions
What imaging modalities are included in this dataset?
It includes CT scans, X-rays, brain MRIs, retinal and gastrointestinal tract images.
Do annotations include image segmentations?
No, the annotations focus mainly on diagnostic classes, without detailed segmentation.
Is the dataset suitable for commercial use?
Yes, the CC BY 4.0 license allows commercial use under the condition of attribution.
