---
title: "2026-09-01 ve2e reusable PI CAI vxData samples"
description: "Added generic model loading and reusable vxData-to-PI-CAI sample generation with a thin benchmark."
image: "https://docs.virdx.dev/img/virdx-social-card.png"
---

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# 2026-09-01 ve2e reusable PI CAI vxData samples

## Work Done

- Replaced the ISUP benchmark with a PI-CAI benchmark and moved vxData cohort preparation, expert lesion-target selection, materialization, and canonical model preprocessing into `generate_picai_sample_from_vxdata`.
- Split the reusable PI-CAI package into schemas, data generation, and model inference modules. Added batched prediction for full-cohort inputs.
- Replaced the EMA-only ClearML loader with `load_clearml_model`, which normalizes embedded configs and loads prediction weights from both EMA and main checkpoints.
- Validated recent real ClearML EMA model `b745c96bbb9f4b50a45bd96d26f201af` and main model `a5677ed0e22a4d669f2d9c2c72ac5ab4`.
- Pushed ve2e commits `9bcf0c91` and `91c5a234` to `feat/isup-inference`.
- Completed real workflow `run-ve2e-picai-source-fix-ptpb2` across 46 eligible cases, including vxData preparation, preprocessing, target construction, inference, shape alignment, finite maps, and probability bounds.

## Lessons Learned: Pitfalls

- Lightning `load_from_checkpoint` restored training-only state and rejected a missing `loss_fn.weight`. Inference should load only `module.model` prediction weights.
- Some otherwise eligible vxData cases have no biopsy/RPE provenance. They cannot populate a target schema restricted to those two values and must be excluded explicitly.
- Taking the first patient IDs from the global non-training query can select no patients from pipeline-configured datasources. A distributed sample across the query ordering produced a representative smoke cohort.

## Lessons Learned: Improvements

- Document that PI-CAI target generation requires non-null biopsy/RPE provenance in addition to expert lesion masks.
- Keep a small cluster smoke workflow for reusable inference that selects a privacy-safe distributed cohort and validates real main and EMA checkpoints.
- Treat ClearML main and EMA payloads as packaging differences only; normalize both to model-only state before inference.

Source: https://docs.virdx.dev/knowledge/inbox/2026-09-01-clinical-ve2e-picai-vxdata-samples/index.mdx