Explainable Screening Algorithms for Oncology Images in Resource-Limited Facilities: A Mixed-Evidence Evaluation

Abstract

Pathology-image classifiers trained on curated slides may fail when tissue preparation, scanner optics and image quality vary between laboratories. This gap has consequences for interpreting performance outside carefully curated evaluation settings. This study aims to determine whether interpretable image models preserve diagnostic reliability across acquisition settings. The evaluation would follow a cross-site image-validation design using stratified slide collections and blinded review of model explanation maps. The choice of evaluation units and exclusion criteria is informed by the broader methodological record [3–7]. The analysis will contrast a conventional image classifier, an explanation-augmented classifier and readers reviewing the same slides. The mixed-evidence component combines quantitative tests with an independently coded practitioner evaluation. The technical pipeline must remain auditable: prior work on storage operations provides system-level context [1], while a second source identifies infrastructure conditions relevant to data continuity [2], rather than patient-level outcomes. Assessment will focus on site-specific sensitivity, calibration, false-negative rates and agreement between highlighted regions and pathology annotations. The supporting evidence base motivates the chosen comparator conditions and robustness criteria [8–12]. Critical sensitivity checks address patient-level separation, stain perturbations, class imbalance and disagreements between expert reviewers. Further systems and modelling literature supplies context for the proposed sensitivity analyses [13–17]. Differences between numerical metrics and professional judgements would be retained in the interpretation. The expected output is a reusable evaluation framework with transparent uncertainty and reporting rules, informed by wider reproducibility literature [18–23].