Inspected src/ve2e/training/metrics.py, the validation accumulation and callback code, dataset metadata flow, inference scripts, and inference tests.
Identified the shared Gleason-logit-to-ISUP functions and the current quadratic Cohen kappa implementations.
Confirmed that patient_id and ISUP labels originate in the infofile, while the slice inference script currently groups by vol_id despite calling the result per-patient.
Recommended an importable patient-level ISUP/QWK subroutine in src/ve2e/inference/metrics.py with explicit aggregation and edge-case handling.
No ve2e files were edited.
Lessons Learned: Pitfalls
The knowledge skill requires a clinical workstream index and SOP index, but those paths do not exist in the current knowledge checkout.
Existing code uses “patient-level” for some metrics that are still sample- or volume-level. Names alone are not enough to infer aggregation granularity.
Lessons Learned: Improvements
Add a clinical workstream landing page and SOP index to the knowledge base.
Document the intended hierarchy of slice, volume, and patient aggregation for ve2e evaluation, including how conflicting ISUP labels across volumes are resolved.
