Longitudinal multi-modality radiology with linked reports, 3+ years per patient

Overview

De-identified radiology datasets for commercial AI training and evaluation. Scope: - DICOM across CT, MRI, X-ray, ultrasound, PET/CT and mammography - At least 3 years of longitudinal imaging history per patient - Linked radiology reports and clinical metadata - Follow-up imaging, treatment history and disease progression where available - Annotations or labels where they already exist Structure: dataset licensing or a secure data-access arrangement, under NDA and appropriate data-protection agreements. Volume is open. The buyer scopes by dataset size, quality, longitudinal depth and commercial rights rather than by a fixed count, with a stated acquisition budget of up to 10 million dollars. The quantities on this request are indicative. Suited to hospitals, diagnostic centres and radiology groups with a deep PACS archive rather than a single-modality extract.

Progress

Data Specifications

Use Cases

• •Training and validating Medical imaging AI/ML models
• •Benchmarking Medical imaging detection and segmentation algorithms
• •Building de-identified Medical imaging research datasets for academic studies
• •Augmenting existing Medical imaging datasets to reduce class imbalance