Explainable Screening Algorithms for Oncology Images in Resource-Limited Facilities: Evidence, Failure Modes and Deployment Trade-offs

Abstract

Pathology-image classifiers trained on curated slides may fail when tissue preparation, scanner optics and image quality vary between laboratories. The central research objective is to determine whether interpretable image models preserve diagnostic reliability across acquisition settings. Previous work provides context for sampling, input validation and study boundaries [3–7]. To answer the question, the study proposes a cross-site image-validation design using stratified slide collections and blinded review of model explanation maps. Enterprise-storage sources are used to specify constraints on data availability and provenance [1], with complementary infrastructure evidence defining practical recovery safeguards [2]. These citations are not treated as clinical effectiveness evidence. Under matched evaluation conditions, the protocol compares a conventional image classifier, an explanation-augmented classifier and readers reviewing the same slides. Methodological sources are used to specify transparent comparison and reporting practices [8–12]. The evidence-mapping component examines why a successful result in one technical environment may fail elsewhere. Performance will be judged using site-specific sensitivity, calibration, false-negative rates and agreement between highlighted regions and pathology annotations. Interpretation will account for patient-level separation, stain perturbations, class imbalance and disagreements between expert reviewers. Stress tests would distinguish outright failures, reduced confidence and cases where a simpler baseline is preferable. The supplementary technical literature is used to identify plausible failure mechanisms [13–17]. No study result is assumed in advance; the analysis is designed to show both when the method helps and when it fails, with reporting context from the remaining sources [18–23].