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Integrity Review Report: Pathology-Aware Reconstruction with Discriminative Knowledge Boosting Alignment for CheXray Vision-Language Pre-training (DOI: 10.1145/3746027.3755336)

Academic fraud report · Geng Detector

Summary

This report assesses a paper published at ACM MM '25 (DOI: 10.1145/3746027.3755336) titled "Pathology-Aware Reconstruction with Discriminative Knowledge Boosting Alignment for Che-Xray Vision-Language Pre-training" by Lihong Qiao et al. The overall verdict is highly suspicious. Four issues are flagged: (1) systematic reference mislabeling in Sections 2.1, 5.2, and 5.4.2, where baseline models are cited under wrong reference numbers (e.g., Med-UniC attributed to [24] which is actually SAT [Improving medical vision-language contrastive pretraining with semantics-aware triage], and MGCA attributed to [12] which is actually He et al.'s MAE); (2) implausible zero-gain behavior in Table 1, where MRM and the proposed model both show identical AUC values across 1% and 10% training data on CheXpert (88.5 and 89.5 respectively), violating expected scaling behavior; (3) unresolved Unicode glyph artifacts (e.g., �) in Section 3.1, atypical of a final ACM publication; (4) run-on numeric values in Table 1 (e.g., 82.784.4, 90.891.993.193.780.389.594.5) suggesting poor typesetting. Confidence in findings 1, 2, and 4 is high based on direct textual evidence; finding 3 is corroborated. Limitations: authors have not been contacted; raw training logs were not independently verified.

Verdict

Highly suspicious. Multiple independent integrity indicators are present, including systematic citation mislabeling, implausible tabular results, and poor typesetting quality inconsistent with a finalized ACM MM '25 paper.

Key findings

  • Systematic citation mislabeling (Finding 1, High severity): In Section 2.1, the paper attributes Med-UniC to reference [24], but [24] corresponds to the SAT paper ("Improving medical vision-language contrastive pretraining with semantics-aware triage"); the actual Med-UniC entry is [34]. In Sections 5.2 and 5.4.2, MGCA is attributed to [12], which is He et al.'s MAE; the correct MGCA entry is [35]. The pattern indicates careless citation handling rather than isolated typos.
  • Implausible zero-gain in Table 1 (Finding 2, High severity): On CheXpert AUC, MRM shows 88.5 at both 1% and 10% training data, and the proposed "Ours" model shows 89.5 at both 1% and 10% training data, rising only at 100% (88.7 and 89.7 respectively). A 10× increase in labeled medical imaging data yielding a 0.00 absolute AUC change, repeated identically across two distinct models, is highly anomalous and suggestive of copy-paste tabulation.
  • Unicode rendering artifacts in Section 3.1 (Finding 3, Medium severity): Strings such as ��� and � appear undecoded. Such entities are uncharacteristic of a final accepted ACM publication and indicate either LaTeX-to-HTML/PDF conversion errors or sloppy paste operations during manuscript preparation.
  • Run-on numeric values in Table 1 (Finding 4, Medium severity): ViT-based rows contain concatenated values (e.g., 82.784.4, 90.891.993.193.780.389.594.5, 89.589.792.1), with multiple RSNA and COVIDx entries merged per cell. This is inconsistent with rigorous academic typesetting.
  • Evidence highlights

  • Direct cross-reference: [24] → SAT; [12] → MAE; [35] → MGCA, contradicting in-text attribution.
  • Table 1 CheXpert AUC, MRM: 88.5 (1%) vs 88.5 (10%) vs 88.7 (100%).
  • Table 1 CheXpert AUC, Ours: 89.5 (1%) vs 89.5 (10%) vs 89.7 (100%).
  • Encoded entities �, �, �, � present in Section 3.1.
  • Concatenated cell values in Table 1 ViT-based rows as listed above.
  • DOI: 10.1145/3746027.3755336.
  • Notes

  • Confidence is high for Findings 1, 2, and 4 as they are verifiable from the published text. Finding 3 is also directly observable but may in principle be reproduced by certain rendering pipelines; however, its presence in a camera-ready venue is unusual.
  • Authors have not been contacted, and independent raw training logs were not obtained. Statistical verification of the zero-gain claim (e.g., bootstrapped confidence intervals) was not performed.
  • Findings 1 and 2 together are the strongest integrity indicators; Findings 3 and 4 are corroborating concerns rather than conclusive evidence of misconduct on their own.
  • The report is AI-assisted and intended for academic discussion only; formal adjudication requires institutional investigation.

Tags

#academic-integrity#citation-mislabeling#data-anomaly#suspicious-tabular-results#publication-typesetting#medical-imaging#vision-language-pretraining#acm-mm-2025

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