Pith Number

pith:SQMGTQVJ

pith:2026:SQMGTQVJQAAAIK2G7QI7YFROLA

not attested not anchored not stored refs resolved

M3Net: A Macro-to-Meso-to-Micro Clinical-inspired Hierarchical 3D Network for Pulmonary Nodule Classification

Dianlong Ge, Jingjing Yang, Jinyue Li, Meng Fu, Qiankun Li, Shuyao He, Xin Ning, Yani Zhang, Yannan Chu, Yuzhou Yu

M3Net classifies pulmonary nodules more accurately by processing CT scans in a radiologist-inspired macro-to-micro hierarchy.

arxiv:2605.12570 v1 · 2026-05-12 · cs.CV

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Record completeness

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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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Claims

C1strongest claim

Extensive experiments on the public LIDC-IDRI dataset and a self-collected clinical dataset (USTC-FHLN) demonstrate that our method achieves state-of-the-art performance, with accuracies of 86.96% and 84.24% respectively, outperforming the best baseline by 3.26% and 2.17%.

C2weakest assumption

That the progressive multi-scale input construction together with latent space projection and mutual information maximization yields clinically meaningful accuracy gains that generalize beyond the tested datasets and baselines.

C3one line summary

M3Net achieves state-of-the-art accuracies of 86.96% on LIDC-IDRI and 84.24% on USTC-FHLN for pulmonary nodule classification using a hierarchical multi-scale 3D network with cross-scale consistency.

References

61 extracted · 61 resolved · 2 Pith anchors

[1] , 2023. Expanding role of advanced image analysis in CT-detected indeterminate pulmonary nodules and early lung cancer characteri- zation, author=Prosper, Ashley Elizabeth and Kammer, Michael N and Ma 2023

[2] , 2024. Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images, author=Zhang, Rui and Wei, Ying and Wang, Denian and Chen, Bojiang and Sun, Huaiqiang 2024

[3] , 2024. Lung nodule malignancy classification with associated pulmonary fibrosis using 3D attention-gated convolutional network with CT scans, author=Liu, Yucheng and Hsu, Hao Yun and Lin, Tiffany and 2024

[4] Artificialneuralnetworkvalidationofmhdnaturalbioconvectionina squareenclosure:entropicanalysisandoptimization.ActaMechanica Sinica 41, 724507 2025

[5] The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed referencedatabaseoflungnodulesonCTscans 2011

Receipt and verification

First computed	2026-05-18T03:10:01.765277Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

941869c2a98000042b46fc11fc162e58282e5b7536e23aaeb0eb88388eb01428

Aliases

arxiv: 2605.12570 · arxiv_version: 2605.12570v1 · doi: 10.48550/arxiv.2605.12570 · pith_short_12: SQMGTQVJQAAA · pith_short_16: SQMGTQVJQAAAIK2G · pith_short_8: SQMGTQVJ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/SQMGTQVJQAAAIK2G7QI7YFROLA \
  | 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: 941869c2a98000042b46fc11fc162e58282e5b7536e23aaeb0eb88388eb01428

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "d29bee8b9bf17c2b61ef0a25f441172f3ec8d17a886c43990801ceffd1c67a49",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-12T10:16:41Z",
    "title_canon_sha256": "7b478c5bd9b1c4fe8388a9e97d6d8156a8397f915449c464f5bee98e9b797258"
  },
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  "source": {
    "id": "2605.12570",
    "kind": "arxiv",
    "version": 1
  }
}