Pith Number

pith:RZKPQ6Q3

pith:2026:RZKPQ6Q3VBCODOTRWSBHO6QHUF

not attested not anchored not stored refs resolved

Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation

Guowei Zou, Haitao Wang, Hejun Wu, Zhiquan Chen

A dispersive loss treating batch representations as negatives produces boundary-aware embeddings that improve fine-grained medical image segmentation.

arxiv:2605.14579 v1 · 2026-05-14 · cs.CV

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\pithnumber{RZKPQ6Q3VBCODOTRWSBHO6QHUF}

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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

The Dispersive Loss enlarges inter-sample margins by treating in-batch hidden representations as negative pairs, producing well dispersed and boundary aware embeddings with negligible overhead.

C2weakest assumption

That treating in-batch representations as negative pairs will consistently enlarge margins in a way that improves anatomical boundary delineation rather than simply increasing feature variance without semantic benefit.

C3one line summary

Med-DisSeg uses a dispersive loss on batch representations plus adaptive multi-scale decoding to achieve state-of-the-art fine-grained segmentation on five medical imaging datasets.

References

18 extracted · 18 resolved · 4 Pith anchors

[1] TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation 2020 · arXiv:2102.04306

[2] Expert Systems with Applications 238:122347 2023 · doi:10.1016/j.eswa

[3] Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic) 2018 · arXiv:1902.03368

[4] Cpfnet: Context pyramid and feature fusion network for medical image segmentation. Neurocomputing 376, 222–235. Gu,Z.,etal.,2019. Ce-net:Contextencodernetworkfor2dmedicalimage segmentation. IEEE Trans 2019

[5] Skin lesion analysis toward melanoma detection: A challenge at the isic 2016, in: IEEE International Symposium on Biomedical Imaging (ISBI) Challenge, pp. 1–10. Hatamizadeh, A., Tang, Y., Nath, V., Ya 2016

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:05.385486Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8e54f87a1ba844e1ba71b482777a07a15196041b5dd0d815ab4d9faf20dc0864

Aliases

arxiv: 2605.14579 · arxiv_version: 2605.14579v1 · doi: 10.48550/arxiv.2605.14579 · pith_short_12: RZKPQ6Q3VBCO · pith_short_16: RZKPQ6Q3VBCODOTR · pith_short_8: RZKPQ6Q3

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/RZKPQ6Q3VBCODOTRWSBHO6QHUF \
  | 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: 8e54f87a1ba844e1ba71b482777a07a15196041b5dd0d815ab4d9faf20dc0864

Canonical record JSON

{
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    "abstract_canon_sha256": "e0dd9f3c99fd87044bb82bf81627e519e88e82a2db19dc4dbfda9e5314c392ae",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T08:49:36Z",
    "title_canon_sha256": "aa5e65bbe7efe588f59f9e99497a2ee1bbaf9becd59962d8b79082f5350b1d8a"
  },
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  "source": {
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    "kind": "arxiv",
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}