covtoken

Chucks90/covtoken

covtoken is a machine learning model from Chucks90. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.

Code, gated evaluation, and a working paper draft for a label-free token-pruning method on frozen self-supervised medical vision transformers.

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Updated Jun 27, 2026

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Library: pytorch
License: mit
Access: Public
Created: Jun 20, 2026
Updated: Jun 27, 2026
SHA: b30c499e

Model card

From the Hugging Face model README

covtoken — Label-Free Lesion-Subspace Token Economy for Medical Imaging

Code, gated evaluation, and a working paper draft for a label-free token-pruning method on frozen self-supervised medical vision transformers.

Reframed contribution (see gate_reports/SUMMARY.md). The load-bearing result is a label-free lesion subspace — a mid-layer geometry that localizes lesions WITHOUT labels across anatomy, modality (CT + ultrasound), and backbone (MedDINOv3 + DINOv2) — together with membership pruning (beats saliency pruning on small-lesion miss-rate), a conformal retention certificate, and lesion-routed depth (1.6× FLOPs). The original coverage-constrained optimization with an interpretable dual is reported as a clean negative result with a transferable mechanism (rank-based coverage rewards diverse spanning, rare pathology needs concentration). See gate_reports/NEGATIVE_RESULT.md.

Headline numbers


Mid-layer finding	lesion AUROC final-layer 0.565 → block-3 0.871
Cross-modality	density-A: lung CT 0.87, kidney 0.82, breast US 0.73 (DINOv2; attention 0.49)
Membership > saliency pruning	LIDC +27.6/+15.8, KiTS23 +7.4, BUSI +13.8/+19.0 pts
Conformal retention cert.	empirical coverage 0.978 ≥ nominal 0.90
Lesion-routed depth	1.6× FLOPs at 98% small-lesion sensitivity
Negative result	coverage floor 0.22 vs membership 0.82 (small-lesion recall)

Layout

subspace/     label-free lesion subspace: density (A) + residual (B) constructions
coverage/     rankme / coding-rate / energy functionals (the FALSIFIED coverage objective)
gate/         constrained pruner (Gumbel mask + dual) + per-image certificate  [negative result]
arch/         conformal_head, routed_depth, volumetric  (Phase-6 components)
backbone/     frozen MedDINOv3 ViT-B/16 loader (DINOv2 used for ultrasound, in jobs/)
data/         CT token-bank builder, eval-only mask loading + label-leak guard
eval/         DeLong / paired-bootstrap / Spearman stats; gate runners
jobs/         all experiments as Hugging Face Jobs (materialize, banks, gates, ablations)
gate_reports/ machine-readable per-gate decision records + SUMMARY + NEGATIVE_RESULT
configs/      thresholds.lock.json (Phase-1b calibrated, locked)
paper/        working_draft.md + figures/ + venue_notes.md
tests/        CI label-leak test (no label may touch subspace construction)

Reproducibility

All experiments ran as Hugging Face Jobs. Backbones: ricklisz123/MedDINOv3-ViTB-16-CT-3M (CT), facebook/dinov2-base (ultrasound). Datasets: LIDC-IDRI, KiTS23, MSD Task03 Liver, MSD Task07 Pancreas, BUSI. Masks are evaluation-only; tests/test_label_leak.py fails the build if a label reaches subspace construction. The frozen DINOv3 model code is installed from github.com/facebookresearch/dinov3 at job time (not vendored here).

Decision records: gate_reports/. Locked thresholds: configs/thresholds.lock.json.

Library: pytorch
License: mit
Created: Jun 20, 2026
Updated: Jun 27, 2026