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Medical imaging accounts for the majority of structured clinical data, and yet it remains underutilized for AI training. Seen Labs annotates medical images at scale—from pathology slides to diagnostic scans—ensuring pixel-level accuracy and clinical relevance for health AI development.

Structured Imaging for AI Development

High-quality image annotation is essential for developing robust diagnostic algorithms, surgical support tools, and clinical decision systems. Seen Labs helps clients build clean, well-labeled datasets across a wide range of imaging modalities, both standard and specialized.

Clinical Imaging Use Cases We Support

Our annotators work with radiologists, pathologists, and clinical teams to structure imaging datasets that support classification, detection, segmentation, and measurement tasks.

• X-rays & CT scans: Identify and segment lung nodules, fractures, septal lines, tumors, and cavities  
• Ultrasound: Annotate pleural line changes, b-lines, gallstones, and fetal positioning  
• Pathology slides: Identify mitotic figures, classify tumor grade, and distinguish cancerous from healthy tissue  
• MRI & PET: Localize lesions and anomalies in neurological and cardiovascular scans  
• Skin and facial images: Tag dermatologic findings, wound characteristics, and skin tone variation

Built to Manage Imaging Complexity

Medical imaging data can vary in resolution, color profiles, modalities, and formats. Seen Labs supports annotation at multiple levels of granularity—from bounding boxes to pixel-level masks—and adapts to each client’s clinical or research goals.

• Semantic segmentation and instance labeling  
• Classification of anatomical structures and findings  
• Color matching, lesion severity scoring, and zone-based tagging 

Clinical-Grade Review and Scalability

All annotations undergo quality assurance led by trained medical reviewers. Our systems are designed to process thousands of images weekly while maintaining consistency and traceability across large datasets.

Multi-Modality and Workflow Integration

We work across image, text, waveform, and video to help clients unify data sources for multimodal model training. Seen Labs outputs structured, export-ready formats that plug into enterprise data pipelines and ML frameworks.

Supporting Diagnostic, Research, and Regulatory Work

From AI-powered triage to radiology model validation and pathology automation, Seen Labs helps healthcare organizations and AI companies develop imaging datasets that meet real-world clinical and regulatory expectations.