Artificial Intelligence in Digital Pathology: with a Focus on Breast Cancer

This thesis presents a multimodal evaluation framework and contributes to technical and medical insights to the field. In the first study a research database was constructed to support generalizable AI development, compromising six annotated imaging collections, comprising 754 WSIs and 24,043 pathology annotations, 110 radiology cases and 397 lesion annotations.

One study evaluated a human-in-the-loop (HITL) workflow, when pathologist assess cellproliferation as expressed by Ki-67 from an AI result. Even though AI showed reduced cell-level performance on local data, (F1 score 0.68 compared to 0.83), status agreement for visual estimation of Ki-67 performed significantly worse (Cohen’s κ 0.62) than digital image analysis (κ 0.84)  and HITL (κ 0.76). HITL reduced variability of the Ki-67 error and mitigated key limitations such as tumour heterogeneity, misidentification, and staining variability, while highlighting risks from user handling errors.

Subsequent studies introduced Feature Enhancing Zoom (FEZ), a visualization technique that amplifies stain patterns at low magnification. In a study with eight pathologists, FEZ improved task efficiency by 15% without compromising accuracy. A usability study with 16 pathologists confirmed high ratings, especially for stains requiring small object identification.

Finally, a methodology was evaluated to mitigate domain restraints during clinical implementation of a pretrained AI model for detecting metastases in lymph nodes. A locally curated dataset of 396 cases (4,462 WSIs) was used, with slides labelled by surgical procedure and lesion presence. Results showed that surgical procedure affects model performance, and retraining significantly improved generalization and thus reducing false positive predictions.

In summary, this thesis demonstrates how AI and digital pathology can be integrated into clinical workflows to enhance diagnostic precision.