TETRA Digital
Tissue Library
A structured biomedical imaging resource for tissue characterisation, cancer research, metamaterial-inspired modelling and responsible AI development.
Research-use resource. AI-ready and validation labels describe dataset curation status; they do not constitute clinical certification.
Explore the tissue library
Search by tissue, diagnosis, modality, preparation method or validation state. Replace the demonstration records with approved D1.2/TETRA data through assets/data.js.
Uploaded Retina Collection: 195 image assets were detected (182 JPEG slices and 13 TIFF/TIFF stacks). The library below uses real representative slices from the uploaded archive. Records are labelled Research because diagnosis, magnification, ethics, patient identifiers and clinical validation metadata were not supplied in the archive.
From biological image to translational evidence
The library separates raw observations, expert interpretation, computational features and validation evidence so every downstream result remains traceable.
Tissue image
Microscopy / histopathology with acquisition metadata.
Expert annotation
Regions of interest and pathology labels with provenance.
TETRA descriptors
Morphology, scattering, optical and effective-medium features.
AI analysis
Versioned model outputs, confidence and reproducibility data.
Validation
Independent test cohorts and clearly declared scope.
Every image should be more than a picture.
A strong TETRA library records how a sample was prepared, acquired, interpreted and approved for reuse. This protects scientific reproducibility and makes future AI validation possible.
Keep D1.2 visible — but separate from the living library.
The formal deliverable remains citable and fixed. The Digital Tissue Library is the evolving discovery layer built around versioned, approved records.
Grow the TETRA collection responsibly.
Use a standard submission record for new partner images, metadata, annotation files and reuse permissions.