Explainable Screening Algorithms for Oncology Images in Resource-Limited Facilities: A Reproducibility-Centred Evaluation
DOI:
https://doi.org/10.66687/fc3ztb52Keywords:
Artificial Intelligence, Machine Learning, Medical Informatics, Decision Support Systems, Clinical, Reproducibility of ResultsAbstract
Pathology-image classifiers trained on curated slides may fail when tissue preparation, scanner optics and image quality vary between laboratories. The difficulty is particularly visible when a system is transferred to a new setting. To address this, the study will determine whether interpretable image models preserve diagnostic reliability across acquisition settings. The research design centres on 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 reproducibility extension records dataset versions, model settings, exclusion rules and provenance before any held-out evaluation. 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. 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]. An independent replication exercise would repeat the main analysis from the documented artefacts and quantify disagreements. The expected output is a reusable evaluation framework with transparent uncertainty and reporting rules, informed by wider reproducibility literature [18–23].
References
[1] Mohammed Nazir. (2022). Automated SnapMirror Migration from On-Premises Storage to Cloud Volumes ONTAP: A Replication Performance Study. Journal of Multidisciplinary Research and Integrated Sciences, 2(1), 61–72. https://jmris.com/index.php/jmris/article/view/2022V1P6
[2] Mohammed Nazir. (2021). Automating Enterprise Storage Provisioning with PowerShell and Python: Effects on Deployment Time and Configuration Errors. Journal of Multidisciplinary Research and Integrated Sciences, 1(1), 43–55. https://jmris.com/index.php/jmris/article/view/2021P1V6
[3] McKinney, S. M., Sieniek, M., Godbole, V., et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577, 89–94. https://www.nature.com/articles/s41586-019-1799-6
[4] Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115–118. https://www.nature.com/articles/nature21056
[5] Johnson, A., Pollard, T., & Mark, R. (2016). MIMIC-III Clinical Database, version 1.4. PhysioNet. https://physionet.org/content/mimiciii/1.4/
[6] Singhal, K., Azizi, S., Tu, T., et al. (2023). Large language models encode clinical knowledge. Nature, 620, 172–180. https://www.nature.com/articles/s41586-023-06291-2
[7] Begum, M. H., Talukder, S. I., Ahmed, M. J., Khan, M. F. I., & Azam, M. A. (2025). XAI-Driven Multimodal Deep Learning for Early Sepsis Prediction in ICU. Journal of Science Technology and Social Transformation, 1(1), 30–41. https://jstst.info/j/article/view/25
[8] Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583–589. https://www.nature.com/articles/s41586-021-03819-2
[9] Liu, X., Faes, L., Kale, A. U., et al. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nature Medicine, 26, 1364–1374. https://www.nature.com/articles/s41591-020-1034-x
[10] Guo, C. et al. (2017). On Calibration of Modern Neural Networks. Proceedings of Machine Learning Research, 70, 1321–1330. https://proceedings.mlr.press/v70/guo17a.html
[11] Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1705.07874
[12] Azam, M. A., Hossain, I., & Islam, M. (2024). Advancing Explainable Artificial Intelligence and Predictive Business Analytics to Strengthen Digital Transformation, Operational Resilience, and Productivity among U.S. Small and Medium-Sized Enterprises. European Journal of Management, Economics and Business, 1(1), 70–94. https://ejmeb.com/index.php/journal/article/view/323
[13] Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762
[14] Khan, M. F. I., Begum, M. H., Rahman, M. A., Limon, G. Q., Azam, M. A., & Masum, A. K. M. (2025). A comprehensive review of advances in transformer, GAN, and attention mechanisms: Their role in multimodal learning and applications across NLP. International Journal of Science and Research Archive, 15(1), 454–459. https://ijsra.net/content/comprehensive-review-advances-transformer-gan-and-attention-mechanisms-their-role
[15] Vrable, M., Savage, S. & Voelker, G.M. (2012). BlueSky: A Cloud-Backed File System for the Enterprise. USENIX FAST '12. https://www.usenix.org/conference/fast12/bluesky-cloud-backed-file-system-enterprise
[16] Li, X. et al. (2023). ParaRC: Embracing Sub-Packetization for Repair Parallelization in MSR-Coded Storage. USENIX FAST '23. https://www.usenix.org/conference/fast23/presentation/li-xiaolu
[17] Burke, Q. et al. (2025). On Scalable Integrity Checking for Secure Cloud Disks. USENIX FAST '25. https://www.usenix.org/conference/fast25/presentation/burke
[18] Bai, W. et al. (2023). Empowering Azure Storage with RDMA. USENIX NSDI '23. https://www.usenix.org/conference/nsdi23/presentation/bai
[19] Mihailescu, M., Soundararajan, G. & Amza, C. (2013). MixApart: Decoupled Analytics for Shared Storage Systems. USENIX FAST '13. https://www.usenix.org/conference/fast13/technical-sessions/presentation/mihailescu
[20] Jiao, Z. et al. (2024). The Design and Implementation of a Capacity-Variant Storage System. USENIX FAST '24. https://www.usenix.org/conference/fast24/presentation/jiao
[21] Wu, K. et al. (2021). The Storage Hierarchy is Not a Hierarchy: Optimizing Caching on Modern Storage Devices with Orthus. USENIX FAST '21. https://www.usenix.org/conference/fast21/presentation/wu-kan
[22] Darban, Z.Z. et al. (2022). Deep Learning for Time Series Anomaly Detection: A Survey. arXiv:2211.05244. https://arxiv.org/abs/2211.05244
[23] Li, N. et al. (2022). Speculative Recovery: Cheap, Highly Available Fault Tolerance with Disaggregated Storage. USENIX ATC '22. https://www.usenix.org/conference/atc22/presentation/li-nanqinqin
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