Medical Imaging & Radiology AI
Building AI that sees what radiologists see — at scale, at speed, without error.
Industry Challenge
Radiology departments generate millions of DICOM studies every year — CT scans, MRIs, X‑rays, ultrasounds, and mammograms — yet the volume of images far outpaces the capacity of human radiologists to review them. AI‑assisted radiology promises to reduce reporting backlogs, flag urgent findings, and improve diagnostic accuracy. But every imaging AI model must be trained on thousands of expert‑annotated scans before it can perform reliably. Acquiring that annotation volume from qualified imaging professionals — at clinical‑grade quality — is the central bottleneck.
How SCILabel Serves This Industry
Data Collection
We source de-identified DICOM imaging studies from hospital and radiology centre partners across our global network through direct purchase and revenue-sharing agreements. Data types include CT, MRI, plain X‑ray, ultrasound, mammography, fluoroscopy, and nuclear medicine studies. All datasets are de-identified to HIPAA Safe Harbor or Expert Determination standards and supplied with full provenance documentation.
Data Annotation & Labeling
Our Track 2 (Medical Imaging) workforce — qualified radiographers, imaging technologists, and radiology-trained clinicians — annotates images using bounding boxes, polygons, semantic segmentation, instance segmentation, and classification labels. We annotate organs, anatomical landmarks, lesions, nodules, consolidations, fractures, effusions, and other findings to client-specified ontologies. Every batch passes peer review, QA review, and PM spot-check before delivery.
Data & Model Evaluation
Radiologist-expert evaluators assess model sensitivity, specificity, and false-positive rates against gold-standard annotated test sets. We also perform clinical accuracy benchmarking against established diagnostic guidelines, bias testing across demographic groups, and red-teaming for failure modes.
Annotation Types & Formats
- Bounding box annotation of pulmonary nodules on CT slices
- Organ segmentation (liver, spleen, kidney, heart) on MRI
- Fracture and abnormality classification on plain X‑rays
- Dense lesion segmentation on mammography
- Windowing and multi-frame DICOM navigation
- Anatomical landmark labeling for surgical planning AI
Specialist Workforce Tracks
Track 2 (Medical Imaging): Radiographers, imaging technologists, radiologists, and radiology nurses. Clinical Officers and Medical Doctors also contribute to classification and clinical finding labeling tasks.