Pith Number

pith:4ZE5Q636

pith:2026:4ZE5Q6363C7QJSGRO3TRM6NE4U

not attested not anchored not stored refs resolved

CA-GCL: Cross-Anatomy Global-Local Contrastive Learning for Robust 3D Medical Image Understanding

Die Dai, Hanwen Zhang, Jiaye Yang, Peng Wang, Qiao Liu, Yao Liu, Yutong Xie

A global contrastive objective separates anatomical categories to stop text embedding collapse in 3D medical vision-language models.

arxiv:2605.13544 v1 · 2026-05-13 · cs.CV

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2 Internet Archive

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

CA-GCL consistently outperforms existing VLP paradigms in zero-shot abnormality detection, achieving superior performance while exhibiting strong cross-dataset generalization. Crucially, CA-GCL reduces performance variance across diverse prompt templates, transforming the collapsed textual similarity distribution into a bell-shaped distribution.

C2weakest assumption

That enforcing global separation between anatomical categories via contrastive objectives will counteract local alignment collapse without degrading fine-grained visual-textual correspondences or introducing new instabilities in the latent space.

C3one line summary

CA-GCL adds global contrastive separation and clinical text augmentation to fine-grained vision-language pretraining, reducing textual embedding collapse and prompt variance in 3D medical image tasks.

References

26 extracted · 26 resolved · 3 Pith anchors

[1] arXiv preprint arXiv:2404.00578 (2024) 2024

[2] Research Square pp 2024

[3] In: European conference on computer vision 2022

[4] In: Proceedings of the IEEE/CVF International Conference on Computer Vision 2025

[5] In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024

Receipt and verification

First computed	2026-05-18T02:44:23.939242Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e649d87b7ed8bf04c8d176e71679a4e53bb8499ee2264dba6f5a89e455cca69c

Aliases

arxiv: 2605.13544 · arxiv_version: 2605.13544v1 · doi: 10.48550/arxiv.2605.13544 · pith_short_12: 4ZE5Q6363C7Q · pith_short_16: 4ZE5Q6363C7QJSGR · pith_short_8: 4ZE5Q636

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/4ZE5Q6363C7QJSGRO3TRM6NE4U \
  | 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: e649d87b7ed8bf04c8d176e71679a4e53bb8499ee2264dba6f5a89e455cca69c

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7465aeb75dc44eaf69b6ed5af7df4dbe8244ca6fa06afc461193238e902fa86a",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T13:54:27Z",
    "title_canon_sha256": "e8c6ad4bf420f1db3ce99e20e0df7d98162996275d8ae588a0028d263f3eed7a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13544",
    "kind": "arxiv",
    "version": 1
  }
}