paired-acquisition-neural-factorization

PA-NF — Paired-Acquisition Neural Factorization

PA-NF is a paired-acquisition representation-learning model that separates frozen pathology features into biological and acquisition branches under scanner-paired supervision. This repository is the trained model artifact; its supported result is a bounded, dataset- and comparator-specific structured- separation result, not evidence of pure factors, complete scanner invariance, clinical utility, or deployment readiness.

The separate MatthewVaishnav/paired-acquisition-factorization-evidence repository contains registered metrics, analyses, manifests, and negative results. This model repository contains the trained PA-NF family and the preprocessing state required to use it.

Method summary

The release object is the complete registered pathoalign_dep20 family from the SCORPION capacity-matched campaign: five slide-blocked folds × seeds 801–805 = 25 fixed-final-epoch checkpoints. The campaign had no model-selection rule and records checkpoint_selection: none; fixed final epoch only. No checkpoint is called “best,” and published test results must not be used to rank the 25 instances post hoc.

Architecture

Each PyTorch checkpoint contains a 1,550,026-parameter ScorpionProjection with:

• a 768 → 512 → 256 biological projection;
• a 768 → 512 → 64 acquisition projection;
• a 320 → 512 → 768 joint reconstruction decoder;
• five-class scanner heads on both branches;
• the registered contrastive, reconstruction, variance, covariance, scanner- adversarial, acquisition-scanner, scanner-dependence, and cross-covariance objectives.

The exact architecture is distributed as scorpion_pathoalign.py. Biological and acquisition latent dimensions are 256 and 64; the hidden dimension is 512.

Intended use

• reproduce the registered SCORPION PA-NF projections with a matching fold and seed;
• audit checkpoint-to-campaign provenance and state-dict identity;
• study paired-acquisition representation behavior on frozen features under a prespecified research protocol;
• apply all 25 co-equal family members, or choose a fold/seed by a rule specified independently of the published test outcomes.

Out-of-scope use

Do not use this release for diagnosis, treatment, patient care, clinical or regulatory decisions, production deployment, or claims of universal scanner harmonization. Do not select a checkpoint using the registered held-out test results. The model does not accept raw pathology images.

Training data

The registered campaign used SCORPION v1: 2,400 spatially aligned H&E patches from 480 regions on 48 original slides, each acquired on five scanners. Folds are blocked by original slide. Each fold's preprocessing statistics were fit on all non-test slides in that registered fold.

Inputs were frozen facebook/dinov2-base 768-D features at immutable Hub revision f9e44c814b77203eaa57a6bdbbd535f21ede1415. The DINOv2 feature archive, SCORPION images, and image paths are not redistributed here.

Evaluation

On the registered SCORPION structured-separation objective, the full model's primary scanner-suppression contrast against the equal-capacity two-branch no-scanner-objectives control was:

full - capacity-matched no-scanner-objectives = -0.3108333333

with fold-aware 95% interval [-0.3346122449, -0.2857872340]. Average same-region retrieval and worst-pair retrieval were preserved within their registered noninferiority margins, while the acquisition branch retained strong scanner information.

This is distinct from the corrected canine fixed five-category estimand, where the neural feature-space representations did not establish an additional improvement over the strongest simple scanner-removal baselines. The SCORPION result does not establish universal superiority; the canine result does not erase the registered SCORPION controlled advantage.

Supported claims

The release supports partial structured separation under the tested paired- acquisition protocol and a controlled comparative advantage over the registered equal-capacity two-branch neural control on the SCORPION objective. It supports reduced linear scanner recoverability in the tissue-oriented branch, preserved registered same-region retrieval noninferiority, and retained scanner information in the acquisition branch.

Unsupported claims

This release does not establish pure biological factors, complete scanner invariance, complete disentanglement, information-theoretic independence, universal superiority, diagnostic improvement, clinical utility, patient benefit, deployment readiness, or regulatory readiness.

Limitations

The model was trained on one small scanner-paired dataset and one frozen feature contract. Its outputs depend on the exact DINOv2 representation and fold-specific standardization. Scanner labels were used during training for the scanner objectives, but are not required by the forward pass at inference. Broader human- tissue, site, device, backbone, and task validation is not supplied by these checkpoints.

Provenance

• training source commit: 0adea50f1ef22865969109f1834a3c175e3f8b43;
• model definition: src/models/scorpion_pathoalign.py at SHA256 528b653459bbdb759c5ca414cf434cf0ad54fba1ef6cbc98e6968c235a3c799f;
• campaign config hash: 59ba04450adf738aa2983dc40ecb4ff09ffa495f13d43c56edde5ec57707a2e1;
• evidence dataset revision: a9853bd32e3b446a97608002f7e5ea12f68f88e1;
• source repository: https://github.com/matthewvaishnav/computational-pathology-research.

model-manifest.json binds every fold, seed, run ID, checkpoint SHA256, source cell-manifest SHA256, preprocessing SHA256, config, and state-dict validation. checksums.sha256 covers the complete released folder. The immutable model Hub revision is recorded only after upload, redownload, and checksum verification.

Citation

Please cite the repository CITATION.cff, the PA-NF foundations manuscript, the SCORPION dataset, and DINOv2. The evidence repository carries the registered result and analysis provenance.

License

The PA-NF model release, including the 25 released checkpoints and the researcher-authored release-specific source/inference code distributed with the model package, is licensed under the Apache License 2.0. See the bundled LICENSE file. The Hugging Face model card therefore uses license: apache-2.0.

The frozen facebook/dinov2-base feature archive and SCORPION source images are not redistributed in this model repository. Those third-party artifacts retain their own upstream terms; this release does not relicense them.

Reproducibility

The checkpoint alone is not sufficient. Use the matching preprocessing/fold_X_standardization.npz, which contains 1 × 768 float32 mean and std arrays fit on that fold's non-test slides.

from pathlib import Path
import numpy as np

from inference import load_panf, project_features

root = Path(".")
# Example coordinate only; fold 0 / seed 801 is not designated as the best model.
model, mean, std, identity = load_panf(root, fold=0, seed=801)
raw_dinov2_features = np.load("my_compatible_features.npy")
outputs = project_features(model, raw_dinov2_features, mean, std)
biological = outputs["biological"]  # [n, 256]
acquisition = outputs["acquisition"]  # [n, 64]

Before use, verify the package and all 25 checkpoint contents:

python tools/huggingface/panf_model_bundle.py verify --bundle build/huggingface/paired-acquisition-neural-factorization
python tools/huggingface/release.py verify-local build/huggingface/paired-acquisition-neural-factorization

Scanner labels are required to reproduce the registered training objectives; they are not inputs to the trained projection at inference.