Cell2Case: A Unified Multi-Task Model from Cell-Level Perception to Patient-Level Diagnosis

Patient-level diagnosis in hematopathology requires integrating diverse cellular evidence distributed across patient-associated microscopic views to distinguish clinically and genetically distinct hematologic conditions. Existing unified hematopathology models recognize individual cells and images, but do not explicitly organize their predictions into patient-level evidence. To bridge this gap, we propose Cell2Case, a unified framework that converts local predictions into reusable cellular evidence and hierarchically aggregates it across microscopic views. Cell2Case adapts detector-anchored cellular predictions while preserving their initial semantics, then binds class, localization, morphology, and contextual features into structured, query-aligned cell representations. These representations encode what each detected cell is, where it occurs, and how it appears, while retaining contextual information for downstream reasoning. They support perception and language tasks and enable hierarchical aggregation from cell-level observations through view-level summaries to patient-level predictions. This design allows rare abnormal events, cellular composition, and morphological variation to jointly inform diagnosis instead of collapsing them into a generic case representation. We evaluate Cell2Case across seven tasks using 46 task–dataset configurations from 36 public hematopathology collections and three patient cohorts. Under a shared evaluation protocol, Cell2Case achieves the highest mean performance among the evaluated baselines on every supported task. For seven-class diagnosis on a patient-disjoint test set pooled from three cohorts, Cell2Case improves balanced accuracy by 10.0 percentage points over the strongest evaluated patient-level baseline.