Cell-scale interpretability in pathology models for biomarkers

The emergence of foundation models in the field of digital pathology is redefining the limits of AI-assisted diagnosis. When we talk about biomarkers, the historical difficulty has not resided so much in the predictive capacity of algorithms, but in the opacity of their decisions. A pathologist needs to understand why a model points to a specific region of a hematoxylin-eosin slide as indicative of a molecular alteration. This is where the concept of cell-scale interpretability makes sense: it is not enough for the system to be correct; it must do so by explaining, cell by cell, what morphological evidence supports its judgment. This approach, materialized in architectures like Hireca and its CytoMap module, represents a paradigm shift from the traditional black boxes of deep learning. The ability to locate signals at the cytological level allows not only for clinical validation of predictions, but also for discovering patterns that might have gone unnoticed by the human eye. For a technology company like Q2BSTUDIO, which develops AI for businesses, this trend opens immense opportunities in creating augmented diagnosis platforms that integrate computer vision, medical image processing, and clinical workflows. The combination of pre-trained foundation models with interpretability modules not only improves accuracy but also paves the way for regulatory certification, an indispensable requirement for any hospital deployment. In this context, custom applications allow these algorithms to be adapted to the specific needs of each laboratory, integrating data from multiple centers and ensuring the traceability of each decision. Infrastructure also plays a critical role: the ingestion, processing, and storage of terabytes of histological images requires a robust cloud platform. Cloud services aws and azure offer the necessary elasticity to train large-scale models without compromising the security of sensitive data. Precisely, cybersecurity becomes a silent but indispensable enabler: any system handling clinical information must comply with regulations such as HIPAA or GDPR, and undergo periodic audits. On the other hand, the ability to monitor the performance of these models in production and visualize concordance metrics with pathologists is enhanced through business intelligence services like Power BI, which allow building real-time dashboards. But the true qualitative leap comes with AI agents, autonomous systems capable of orchestrating complex workflows: from requesting additional stains to generating structured reports. At Q2BSTUDIO we develop custom software that integrates these components, offering laboratories and hospitals a transparent, auditable, and clinically relevant tool. Cell-scale interpretability is not an academic luxury; it is the minimum requirement for a pathologist to trust a model's recommendation. And that trust, built on granular evidence, is what will democratize access to biomarker tests that today are costly or consume valuable tissue. The technology is already mature; now it is time to implement it with judgment, scalability, and, above all, with the patient in mind.