Pith Number

pith:BJX7SPCD

pith:2026:BJX7SPCDNEXURBQ54QJKNSAGBP

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Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation

Bo Peng, Daqian Shi, Honghan Wu, Jing Gao, Johan Thygesen, Kun Wang, Na Wang, Tian Li, Wujian Xu, Ximing Liao, Yingqun Ji

Distilling knowledge from the best CT window transfers latent pathological signatures to students on other windows and raises per-window AUC by 10-16 points.

arxiv:2605.12562 v1 · 2026-05-12 · eess.IV · cs.AI · 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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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

distillation improved per-window AUC by 10.1-16.5 percentage points on COPD-CT-DF (0.75-0.81 to 0.90-0.94; all P<0.001), with ensemble AUC reaching 0.9960. Similar gains were observed on RSNA PE (0.80-0.83 to 0.90-0.92) and CTEPD (AUC 0.7481 vs. 0.6264).

C2weakest assumption

That the teacher model trained on the single most informative window captures all clinically relevant cross-density pathological signatures that can be successfully distilled to student encoders on other windows without introducing bias or loss of information specific to those windows.

C3one line summary

Cross-window knowledge distillation raises per-window AUC by 10-16 points in pulmonary CT by transferring latent pathological signatures from a teacher encoder on the most informative window to students on other windows.

References

22 extracted · 22 resolved · 0 Pith anchors

[1] Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respir Res, 25(1):319, 2024 2024 · doi:10.1186/s12931-024-02913-z

[2] Fundamentals of Radiology 2015

[3] The CT pulmonary vascular parameters and disease severity in COPD patients on acute exacerbation: a correlation analysis.BMC Pulm Med, 21(1):34, 2021 2021

[4] Bartolome R Celli, Marc Decramer, Jadwiga A Wedzicha, Kevin C Wilson, Alvar Agust´ ı, Gerard J Criner, et al. An official American Thoracic Society/European Respiratory Society statement: research que 2015

[5] High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis.NPJ Digit Med, 8(1):254, 2025 2025

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-18T03:10:01.961694Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0a6ff93c43692f48861de412a6c8060bc11696f6aa1607d934fbd512338b9686

Aliases

arxiv: 2605.12562 · arxiv_version: 2605.12562v1 · doi: 10.48550/arxiv.2605.12562 · pith_short_12: BJX7SPCDNEXU · pith_short_16: BJX7SPCDNEXURBQ5 · pith_short_8: BJX7SPCD

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/BJX7SPCDNEXURBQ54QJKNSAGBP \
  | 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: 0a6ff93c43692f48861de412a6c8060bc11696f6aa1607d934fbd512338b9686

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2099a75263083ba9f2a7c93c8bcf3592b4f47542cfc1677772723c01a7bdf489",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.CV"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.IV",
    "submitted_at": "2026-05-12T03:40:38Z",
    "title_canon_sha256": "f2f3f811642975b559580f6755c326bced224e70629501bbe996b9dfe04bf33e"
  },
  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 1
  }
}