---
title: "2026-09-18 NanoVirDx histology preprocessing"
description: "Added a path-backed Histo preprocessing transformer to NanoVirDx and validated its one-to-many WSI output path."
image: "https://docs.virdx.dev/img/virdx-social-card.png"
---

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# 2026-09-18 NanoVirDx histology preprocessing

## Work Done

- Implemented `HistoPreprocessingTransformer` in `packages/nanovirdx` as a thin local `WSIBatch -> WSIBatch` adapter. It lazily imports Histo, builds the Factory preprocessing parameters, calls `preprocess_sample` once per WSI, and translates the result into `PreprocessingOutputBatch`. The output preserves original-coordinate tissue masks on `input_wsi` and groups each source WSI’s component images with their aligned tissue masks. Histo owns model resolution, validation, errors, and artifact creation.
- Kept Factory-owned concerns out of the transformer: cohort selection, vxData identifiers and metadata, uploads, registration, and workflow retry behavior remain Factory responsibilities.
- Implemented `WSIBatch.from_vxdata(...)` to resolve and download HistoScans while preserving requested order.
- Added the internal Artifactory dependency `histo 0.0.127` for the CUDA-enabled Linux platform, updated `virdx-ome` to the API-compatible 0.0.24 series, and regenerated `packages/nanovirdx/pixi.lock`.
- Validated an Otsu synthetic OME-TIFF end to end, including the component TIFF, foreground masks, and transform JSON. Revalidated the existing DiffSim pathway after the dependency update.
- Ran Ruff, Pyright, compileall, Pixi lock checking, diff checking, Nx affected checks, and module-boundary checks. Nx had no registered NanoVirDx tasks; the direct package checks covered the implementation.
- Committed the initial implementation as `2c2d4613`, then removed the redundant wrapper logic and moved the class to `preprocessing.py` in `d88a023b`. Commit `d97e88dd` added the dedicated preprocessing output contract and preserved both original and component tissue masks. All commits were pushed to `origin/fk/nanovirdx`.
- Referenced Factory's `feat/factory-histo` preprocessing service and the Histo `preprocess_sample`, `InputSample`, `TissueDetectionParams`, and GrandQC implementations.

## Lessons Learned: Pitfalls

- The initial adapter duplicated responsibilities already encapsulated by Histo or owned by Factory: cache manifests, artifact completion validation, per-slide error aggregation, parameter checks, and direct GrandQC model loading. This grew a straightforward wrapper to 264 lines. The corrected implementation is 81 lines and delegates those concerns.
- `dev.felixknispel.coder.dev` did not resolve, so the packaged Linux/CUDA runtime could not be executed there. The macOS smoke test used the local Histo source environment (`0.0.120`) and the model-free Otsu detector; packaged Histo `0.0.127` was still resolved and lock-checked from Artifactory.
- Released Histo `0.0.127` remains effectively Linux/CUDA-only because its runtime unconditionally depends on GPU packages. This prevents direct packaged preprocessing validation on macOS.
- Histo tag `0.0.127` still resolves default GrandQC weights from the standard mounted path; the later Hugging Face download work is not an ancestor of that release tag. NanoVirDx therefore documents an explicit `grandqc_model_path` unless the standard mount exists.
- NanoVirDx is not registered as an Nx project, so workspace affected checks report no tasks even though the Python package changed.

## Lessons Learned: Improvements

- Release a slim CPU-capable Histo preprocessing package or feature split. That would allow macOS development and avoid pulling the training and CUDA dependency closure into CPU Factory workers.
- Publish the GrandQC runtime-download change in a new Histo release, then update NanoVirDx's pin and documentation so default setup does not depend on an external mount.
- Add a NanoVirDx `project.json` with lint, typecheck, test, and build targets. Add small behavior-level tests once pre-provided test files are available, especially for grouped one-to-many component outputs, aligned tissue masks, and ordered vxData downloads.
- Keep a small approved WSI fixture or a documented low-volume vxData scan for packaged Linux integration checks. This would avoid downloading gigabyte-scale production slides for routine validation.

Source: https://docs.virdx.dev/knowledge/inbox/2026-09-18-inverse-nanovirdx-histo-preprocessing/index.mdx