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Medical Imaging Datasets & Annotation — Everything PYCAD Has Built
Free dataset resources, annotation services, format conversion guides, and preprocessing workflows — all the data infrastructure PYCAD covers for medical AI projects.
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Sample DICOM Dataset Library
A curated library of publicly available medical imaging datasets for testing, development, and AI model training — filterable by modality, anatomy, and use case.
Browse curated public DICOM and medical imaging datasets. Filter by CT, MRI, PET, X-ray, and ultrasound. Includes download links, caveats, and license notes.
🔄Format conversion workflows for training data pipelines — DICOM to NIfTI for nnU-Net/MONAI, NIfTI to STL for 3D planning, and more. With tooling and caveats.
Services
Data & Annotation Services We Provide
Expert annotation, data curation, and AI-assisted labeling for medical imaging datasets — structured, QA'd, and ready for model training.
Expert annotation of CT, MRI, X-ray, and ultrasound data for model training — structured, QA'd, and delivered in DICOM SEG, NIfTI, or JSON format.
🧠End-to-end AI development including data pipeline design, preprocessing, model training, and deployment — starting from your raw imaging data.
⚙️Deploy trained segmentation models with production-ready preprocessing, inference pipelines, and DICOM-compatible output formats.
In-Depth Guides
Datasets, Annotation & Preprocessing Guides
Practical guides on medical imaging data formats, annotation workflows, preprocessing pipelines, and format conversion for AI training.
Need annotated data or a data pipeline?
We handle annotation, curation, preprocessing, and training data pipelines for medical imaging AI projects — from raw DICOM to model-ready datasets.
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