Pathology AI
Training AI to read the cellular landscape of disease
Industry Challenge
Digital pathology is undergoing a transformation. Whole-slide imaging (WSI) technology now generates gigapixel images of tissue biopsies, enabling AI to assist with tumour grading, cell counting, margin assessment, and biomarker quantification. The challenge is annotation: pathology AI models require thousands of annotated whole-slide images labeled by qualified pathologists or trained pathology technologists — a workforce that is scarce and expensive in most markets.
How SCILabel Serves This Industry
Data Collection
We source whole-slide image datasets from pathology laboratory partners and oncology research centres globally. Data types include H&E-stained slides, immunohistochemistry (IHC), fluorescence microscopy, and cytology specimens. All samples are de-identified with full chain-of-custody documentation.
Data Annotation & Labeling
Our specialist pathology annotators — medical laboratory scientists, biomedical scientists, and clinical officers with pathology training — annotate whole-slide images at the region-of-interest, tissue, and cell level. Annotation types include tissue classification, tumour boundary delineation, mitosis detection, cell-level instance segmentation, and grading (e.g., Gleason grading for prostate cancer). We use specialised WSI viewers and support all major formats (SVS, NDPI, MRXS, TIFF).
Data & Model Evaluation
Expert evaluators assess model performance on tumour grading accuracy, mitotic figure detection precision and recall, and false-positive rates on benign tissue. We conduct comparative evaluations against pathologist ground truth and published grading standards.
Annotation Types & Formats
- Region-of-interest annotation on whole-slide images
- Tumour boundary polygon segmentation
- Cell-level instance segmentation (mitotic figures, lymphocytes, plasma cells)
- Tissue classification (tumour, stroma, necrosis, normal)
- Immunohistochemistry scoring (e.g., HER2, PD-L1 expression)
Specialist Workforce Tracks
Track 2 (Medical Imaging): with pathology specialisation: Medical Laboratory Scientists, Biomedical Scientists, Histology Technologists.