Bounding box annotation for healthcare involves marking rectangular regions around areas of interest in medical images, such as tumors, organs, or anomalies. This technique is essential for training AI models in diagnostics, medical imaging analysis, and automated healthcare solutions.
Segmentation for object classification in healthcare involves precisely outlining and labeling regions in medical images, such as organs, tissues, or abnormalities. This advanced technique enables AI models to perform accurate diagnostics, enhance image analysis, and support personalized treatment planning.
Unitlab supports DICOM, CT, MRI, NIfTI, NRRD, whole-slide pathology, clinical documents, text, images, video, and connected multimodal records.
Medical teams can work with synchronized multiplanar views, volumetric segmentation, clinical properties, timelines, and study-level review for CT and MRI data.
Yes. Whole-slide images can be reviewed with deep zoom, tissue regions, cell and nuclei instances, segmentation, findings, and pathology-specific ontologies.
Yes. Document regions, OCR fields, text entities, relations, classifications, and medical images can be managed in connected multimodal workflows.
Teams can define controlled classes, findings, properties, and relationships so annotations remain consistent across studies, specialists, and dataset versions.
AI-assisted segmentation and model-in-the-loop workflows can accelerate labeling while keeping specialist review and correction in control.
Reviewer roles, issues, rework, approval steps, annotation history, and dataset versions support traceable expert review.
Yes. On-premises deployment is available for organizations that need medical data, models, and workflows inside controlled infrastructure.
Unitlab supports large medical files, related sequences, study context, collaborative review, and versioned datasets for ongoing healthcare AI programs.
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