Pith Number

pith:55KDWYLZ

pith:2026:55KDWYLZ3TT2F5X4QEU5LGF4CW

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SpectraFlow: Unifying Structural Pretraining and Frequency Adaptation for Medical Image Segmentation

Guowei Zou, Haitao Wang, Hejun Wu, Zhiquan Chen

Aligning images and binary masks in a shared latent space through latent transport regression produces transferable structural representations that improve medical image segmentation accuracy and boundary precision in low-data regimes.

arxiv:2605.14566 v1 · 2026-05-14 · cs.CV

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

5 Replications 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

Experiments on ISIC-2016, Kvasir-SEG, and GlaS demonstrate consistent gains over state-of-the-art methods, with improved robustness in low-data settings and sharper boundary delineation.

C2weakest assumption

That aligning images and binary masks through latent transport regression in a shared latent space produces task-agnostic structural representations that transfer effectively to downstream segmentation without bias from the mask generation or pretraining process.

C3one line summary

SpectraFlow combines structure-aware pretraining with mask-guided latent alignment and frequency-directional decoding to improve medical image segmentation accuracy and boundary sharpness in low-data regimes.

References

30 extracted · 30 resolved · 0 Pith anchors

[1] In: ICLR (2022) 2, 5 2022

[2] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation (2021) 2 2021

[3] Caron, M., Touvron, H., Misra, I., J´ egou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: ICCV. pp. 9650– 9660 (2021) 3, 5 2021

[4] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. In: MICCAI. pp. 127–136 (2021) 1 2021

[5] Chen, L., Gu, L., Li, L., Yan, C., Fu, Y.: Frequency dynamic convolution for dense image prediction. In: CVPR. pp. 30178–30188 (2025) 3, 8 2025

Formal links

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Receipt and verification

First computed	2026-05-17T23:39:05.531258Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ef543b6179dce7a2f6fc8129d598bc15bddf7fae30ad0434fa59a2441094d3f4

Aliases

arxiv: 2605.14566 · arxiv_version: 2605.14566v1 · doi: 10.48550/arxiv.2605.14566 · pith_short_12: 55KDWYLZ3TT2 · pith_short_16: 55KDWYLZ3TT2F5X4 · pith_short_8: 55KDWYLZ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/55KDWYLZ3TT2F5X4QEU5LGF4CW \
  | 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: ef543b6179dce7a2f6fc8129d598bc15bddf7fae30ad0434fa59a2441094d3f4

Canonical record JSON

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    "abstract_canon_sha256": "706c4e47727fad6dec3f641bb72fd568f6510da8e020106c6498c3e36af9925a",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T08:37:10Z",
    "title_canon_sha256": "2f7a952336d1404879be6ae69c64a0750c0cedb098460155e91f457a57a30ea7"
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