Pith Number

pith:BVQCOUBM

pith:2026:BVQCOUBMRT42SL6GHJBKFNTKED

not attested not anchored not stored refs resolved

Pyramid Self-contrastive Learning Framework for Test-time Ultrasound Image Denoising

Bingze Dai, Jiajing Zhang, Wei-Ning Lee, Xi Zhang, Yue Xu

Self-contrastive learning on sub-aperture signals produces denoised ultrasound images from a single test sample.

arxiv:2605.12567 v1 · 2026-05-12 · cs.CV · cs.AI

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Claims

C1strongest claim

Our Aperture-to-Aperture (A2A) framework disentangles anatomical similarity and noise randomness from shuffled sub-apertures through self-contrastive learning in pyramid latent spaces. The clean image is then decoded from the anatomy space, while discarding the noise space. Simulation experiments demonstrated an improvement of 69.3% SNR and 34.4% CNR by A2A. The in vivo results showed 84.8% SNR and 25.7% CNR gains using only two aperture data.

C2weakest assumption

That sub-aperture signals in synthetic aperture ultrasound share sufficient anatomical content while differing primarily in independent noise realizations, allowing reliable disentanglement via self-contrastive learning in pyramid spaces even for complex in vivo composite noise without additional regularization or failure modes.

C3one line summary

A2A achieves one-shot ultrasound denoising via pyramid self-contrastive learning on sub-aperture signals to disentangle anatomy from noise, yielding large SNR and CNR gains in simulations and in vivo scans.

References

68 extracted · 68 resolved · 2 Pith anchors

[1] J. A. Jensen, S. I. Nikolov, K. L. Gammelmark, M. H. Pedersen, Syntheticapertureultrasoundimaging,Ultrasonics44(2006)e5–e15 2006

[2] C. Papadacci, M. Pernot, M. Couade, M. Fink, M. Tanter, High- contrastultrafastimagingoftheheart,IEEEtransactionsonultrason- ics, ferroelectrics, and frequency control 61 (2) (2014) 288–301 2014

[3] K. Krissian, R. Kikinis, C.-F. Westin, K. Vosburgh, Speckle- constrained filtering of ultrasound images, in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005

[4] H. Xie, L. E. Pierce, F. T. Ulaby, Statistical properties of logarith- mically transformed speckle, IEEE transactions on geoscience and remote sensing 40 (3) (2002) 721–727 2002

[5] T. Loupas, W. McDicken, P. Allan, Noise reduction in ultrasonic images by digital filtering, The British journal of radiology 60 (712) (1987) 389–392 1987

Formal links

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Receipt and verification

First computed	2026-05-18T03:10:01.839807Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0d6027502c8cf9a92fc63a42a2b66a20c99ce5696ed241011060229238e4a4b8

Aliases

arxiv: 2605.12567 · arxiv_version: 2605.12567v1 · doi: 10.48550/arxiv.2605.12567 · pith_short_12: BVQCOUBMRT42 · pith_short_16: BVQCOUBMRT42SL6G · pith_short_8: BVQCOUBM

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/BVQCOUBMRT42SL6GHJBKFNTKED \
  | 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: 0d6027502c8cf9a92fc63a42a2b66a20c99ce5696ed241011060229238e4a4b8

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7650691334ac4d0d8bbdb821da603a4adf9df0ff0fff7d60c1a3e5c011fd3dc4",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-12T09:27:21Z",
    "title_canon_sha256": "77c5da0b3ac853cbe93fa364ade39354eea4e48b2d4cbb2f28553185be13919a"
  },
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  "source": {
    "id": "2605.12567",
    "kind": "arxiv",
    "version": 1
  }
}