Pith Number

pith:QJQDIAHG

pith:2026:QJQDIAHGXY5DERWW2VI5HOE5ZS

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VoxCor: Training-Free Volumetric Features for Multimodal Voxel Correspondence

Ender Konukoglu, Ertunc Erdil, Guney Tombak

A training-free fit-transform method creates reusable volumetric features from frozen 2D vision transformers for cross-modal voxel correspondence.

arxiv:2605.13798 v1 · 2026-05-13 · cs.CV

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

VoxCor improves the hardest cross-subject, cross-modality transfer settings, reduces encoder sensitivity for dense correspondence transfer, and yields registration performance competitive with handcrafted descriptors and learned 3D features.

C2weakest assumption

That the modality-stable anatomical directions identified by the WPLS projection on fitting-time correspondences will generalize to new volumes and unseen modality combinations without further adaptation.

C3one line summary

VoxCor creates reusable volumetric features from frozen 2D ViT models by combining triplanar inference with a closed-form weighted partial least squares projection, enabling direct voxel correspondence across modalities without training or registration.

References

36 extracted · 36 resolved · 4 Pith anchors

[1] A survey of medical image registration.Medical image analysis, 2(1):1–36, 1998 1998

[2] Deformable medical image registration: A survey.IEEE transactions on medical imaging, 32(7):1153–1190, 2013 2013

[3] A review of atlas-based segmentation for magnetic resonance brain images.Computer methods and programs in biomedicine, 104(3):e158–e177, 2011 2011

[4] Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.Medical image analysis, 12(1):26–41, 2008 2008

[5] Elastix: a toolbox for intensity-based medical image registration.IEEE transactions on medical imaging, 29(1):196–205, 2009 2009

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-18T02:44:15.546508Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

82603400e6be3a3246d6d551d3b89dcc8d95cfe3ff3e6ad745886a7787dc55aa

Aliases

arxiv: 2605.13798 · arxiv_version: 2605.13798v1 · doi: 10.48550/arxiv.2605.13798 · pith_short_12: QJQDIAHGXY5D · pith_short_16: QJQDIAHGXY5DERWW · pith_short_8: QJQDIAHG

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/QJQDIAHGXY5DERWW2VI5HOE5ZS \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 82603400e6be3a3246d6d551d3b89dcc8d95cfe3ff3e6ad745886a7787dc55aa

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "61f6a9294006fa914aa6bcdf63d177fe39ded1409ce93ec5efe14b0cd97654e7",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T17:20:26Z",
    "title_canon_sha256": "a97e9b44c9f17cccb65aa07f95d4245930a7a952ed3e1860069a35259809b470"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13798",
    "kind": "arxiv",
    "version": 1
  }
}