Research on Cervical Cancer p16/Ki-67 Immunohistochemical Dual-Staining Image Recognition Algorithm Based on YOLO

Cai-Jun Zhao; Chun Meng; Hang Wang; Xiao-Jun Wu

REVIEW 4 major objections 4 minor 13 references

Research on Cervical Cancer p16/Ki-67 Immunohistochemical Dual-Staining Image Recognition Algorithm Based on YOLO

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read An improved YOLOv5 detector, DSIR-YOLO, recognizes p16/Ki-67 dual-positive cervical cells at 92.6% mAP@0.5 and 70.5% mAP@0.5:0.95, and the paper reports that cleaning annotation boxes contributes more to the gain than the architectural…

desk verdict Routine architecture stacking with a useful annotation-quality study; the evaluation leaks slide information, so the headline gains are likely overstated. read the letter →

arxiv 2412.01372 v1 pith:ANISUOT6 submitted 2024-12-02 cs.AI

Xiao-Jun Wu , Cai-Jun Zhao , Chun Meng , Hang Wang This is my paper · ORCID

classification cs.AI

keywords cervicalcancerscreeningp16/Ki-67dualstainingimmunocytochemistryYOLOv5objectdetectionSwin-TransformerglobalattentionmechanismEIoUlossdatasetannotationquality

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a specific modification of the YOLOv5 object detector, called DSIR-YOLO, can reliably find cells that show both p16 and Ki-67 staining in cervical cytology images, and that careful cleanup of training annotations contributes as much to performance as the network changes. The authors replace the YOLOv5s backbone with a Swin-Transformer, add a global attention mechanism, multi-scale feature fusion with spatial pyramid pooling, and a Focal-EIoU loss, reporting 92.6% mean average precision at a 0.5 overlap threshold and 70.5% across thresholds 0.5 to 0.95 on their dual-stained dataset. They also report that four annotation-quality fixes alone lift unmodified YOLOv5s by large margins, from 72.4% to 85.7% precision, 69.0% to 84.3% recall, and 38.3% to 68.8% in mAP@0.5:0.95. If these results hold on new slides, they would support computer-assisted reading of p16/Ki-67 dual stains for cervical cancer screening where pathologists are scarce.

What carries the argument

The carrying object is the assembled DSIR-YOLO detector: a lightweight YOLOv5 architecture whose backbone is replaced by a Swin-Transformer module, a visual transformer built from windowed and shifted-window self-attention; augmented with the GAM attention mechanism to preserve spatial and channel interactions; a BiFPN-like multi-scale feature fusion path with spatial pyramid pooling after downsampling; and the Focal-EIoU loss for bounding-box regression. The argument is carried by ablation experiments that add each component to the baseline and show stepwise gains, with the full configuration reaching 91.5% mAP@0.5 and 69.1% mAP@0.5:0.95 before data augmentation and 92.6% and 70.5% with augmentation.

What would settle it

Run the same five-fold protocol but split patches by whole-slide image, so every patch of a given slide goes into exactly one fold, and retrain DSIR-YOLO and YOLOv5s; if the mAP@0.5 gap of 4.3 points and the mAP@0.5:0.95 gap of 8.0 points shrink substantially or reverse, the claimed architectural advantage is an artifact of patch-level leakage rather than generalization.

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Extended reading notes

Core claim

The central claim is that the DSIR-YOLO architecture, an enhanced YOLOv5 with a Swin-Transformer backbone, GAM attention, multi-scale feature fusion with spatial pyramid pooling, and Focal-EIoU loss, detects p16/Ki-67 dual-positive cervical cells better than the original and better than several comparators on this dataset. In five-fold cross-validation the improved model's mean precision, recall, mAP@0.5, and mAP@0.5:0.95 are 86.9%, 84.6%, 92.3%, and 69.2%, against 84.6%, 80.5%, 88.0%, and 61.2% for YOLOv5s, with smaller variances and $p<0.006$. The paper also claims that dataset quality is a major lever: after four annotation fixes, unmodified YOLOv5s improves by 13.3 percentage points in precision, 15.3 in recall, 18.3 in mAP@0.5, and 30.5 in mAP@0.5:0.95.

Load-bearing premise

The results assume that randomly splitting 7,662 image patches from only 11 whole-slide images into training and validation sets measures how the model will perform on new slides, even though patches from the same slide can appear on both sides of the split.

Editorial extensions

If this is right

A screening tool could use DSIR-YOLO to flag dual-positive cells automatically, with the reported 92.3% mean mAP@0.5 across folds reducing the manual search burden on pathologists.
Standardizing annotation boxes, by tightening them, splitting clusters, labelling small positive cells, and avoiding oversized boxes on angled clusters, is itself a performance lever, since the paper shows it lifts YOLOv5s by double-digit percentage points before any architectural change.
The architectural improvements concentrate on small-cell detection and false-positive suppression around clusters, which are precisely the error modes that matter in p16/Ki-67 dual-stain reading.
The improved model shows smaller cross-validation variance than YOLOv5s, so a deployed system should be more consistent when retrained on different clinic samples, provided the split measures slide-level generalization.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial extension: The reported gains are measured on a random patch split, and because the 7,662 patches come from only 11 whole-slide images, a slide-stratified split would reveal how much of the gain is genuine generalization to new slides rather than memorization of slide-specific appearance.
Editorial extension: The large annotation-quality effect suggests a transferable protocol for small-object cytology detection: compare models only on tightly audited labels, because label noise can swamp architectural improvements; this protocol could be tested on other liquid-based cytology datasets.
Editorial extension: The paper notes that the model sacrifices speed for accuracy, so quantifying inference time per whole slide or per patch would let a clinic decide whether DSIR-YOLO can run at screening throughput, a question the paper leaves open.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this is a routine architecture-assembly paper with a genuinely useful dataset-quality study bolted on, and the evaluation doesn't support the headline generalization claim.

What's new: applying Swin-Transformer + GAM + BiFPN-style fusion + EIoU to p16/Ki-67 dual-stained cytology is a new combination, but each piece is published and the assembly is exactly what you'd expect. The annotation-quality analysis is the real contribution: they show that tightening bounding boxes, splitting large clusters, labeling isolated positives, and fixing diagonal boxes lifts YOLOv5s mAP@0.5:0.95 from 38.3% to 68.8% on their data. That's a practical result for anyone building cytology datasets.

What's solid: the experiments are fairly extensive for a private dataset. They run ablations, compare with YOLOv5 variants, YOLOX, YOLOv8, Faster R-CNN, and SSD, and do five-fold CV. The reported differences between DSIR-YOLO and YOLOv5s are consistent across folds in Table 5.

Soft spots: the biggest problem is the patch-level split. 7,662 patches come from only 11 whole-slide images, and the split is random at patch level. Patches from the same slide share staining, background, and annotation noise, so train and validation contain near-duplicates. That leaks slide-specific information and inflates all the metrics. The p-values in Table 5 are computed from five paired folds but don't account for clustering by slide, so they're not evidence of generalization to new slides or patients.

There's also an unexplained inconsistency: YOLOv5s mAP@0.5 is 84.9% in Table 2 and 88.0% in the five-fold mean in Table 5. Same model, same dataset, different numbers. The ablation table's last row adds data augmentation on top of all architectural changes, so the final gain mixes augmentation with architecture. And there's no code or data, so the numbers can't be checked.

Bottom line: the paper is a competent applied effort, the dataset-quality observations are worth publishing, but the central claim—that DSIR-YOLO significantly outperforms YOLOv5s on unseen dual-stained images—is not demonstrated. Who's it for: applied researchers who want a baseline recipe and a reminder that annotation quality matters more than architecture tricks. I'd send it to peer review because the application and the dataset study are legitimate, but the authors need to release data/code and redo the evaluation with slide-level splits before I'd trust the numbers.

Referee Report

4 major / 4 minor

Summary. The manuscript proposes DSIR-YOLO, a modified YOLOv5 model for detecting p16/Ki-67 dual-stained positive cells in cervical cytology images. The modifications are a Swin-Transformer backbone, GAM attention, BiFPN-style multi-scale feature fusion, and EIoU loss. Using a private dataset of 7,662 patches derived from 11 whole-slide images, the authors compare DSIR-YOLO with YOLOv5 variants and other detectors, report ablation results, and analyze how four annotation-quality refinements affect detection. They report mAP@0.5 of 92.6% and mAP@0.5:0.95 of 70.5% and claim consistent gains over YOLOv5s in five-fold cross-validation.

Significance. If the results are valid, the paper offers a practically useful detector for cervical cancer screening and a useful demonstration that annotation quality substantially affects object-detection performance in cytology. The strengths are the systematic study of annotation quality (pixel sealing, scale discrepancies, unlabeled cells, and large-angle clusters), the comparison with several YOLO and non-YOLO baselines, and the explicit attempt at cross-validation. However, the central comparison rests on patch-level splits of only 11 slides, which risks slide-level leakage, and several reported numbers are internally inconsistent. The architectural novelty is modest, but the empirical contribution could be valuable for a clinical engineering audience if these issues are resolved.

major comments (4)

[Section 2.1 and Section 3.3.4] The 7,662 patches are randomly split at an 8:2 ratio and randomly partitioned into five folds without any separation by source whole-slide image or patient. Because only 11 whole-slide images are used, patches from the same slide can appear in both training and validation, sharing staining conditions, background texture, and annotation noise. This near-duplicate content can inflate mAP values and the p-values in Table 5, so the reported results do not yet establish generalization to unseen slides or patients. Please report slide-level or patient-level cross-validation, or explicitly restrict the claims to patch-level recognition within the same slides.
[Table 5 and Table 2] The mean row for YOLOv5s and for 'This Study' in Table 5 is identical to the Fold1 row, while the other folds differ, which is almost certainly a copy-paste error. In addition, the YOLOv5s mAP@0.5 mean in Table 5 (88.0%) contradicts the YOLOv5s mAP@0.5 in Table 2 (84.9%), and the abstract's 'reaching 92.6% and 70.5%' corresponds to the single split in Table 2 while the subsequent 'increased by 2.3%, 4.1%, 4.3%, and 8.0%' corresponds to the cross-validation means in Table 5. The manuscript must state which evaluation is primary and reconcile these numbers, because the central claim depends on a consistent baseline.
[Section 3.3.2, Table 3] The final ablation row combines all modules with 'Data Augmentation,' but Section 2.2 states that Mosaic augmentation, MixUp augmentation, and random horizontal flipping were already used to expand the training dataset. The paper does not define what 'Data Augmentation' adds in this ablation, and no no-augmentation baseline is provided. Consequently, the marginal contribution of the architectural modules is not cleanly isolated, and the attribution of the final gains to the proposed modules is undermined. Please specify the augmentation protocol for every row and hold augmentation fixed across the ablation comparisons.
[Section 3.3.4, Table 5] The p-values are reported for five folds, but no statistical test is named, no test statistic is given, and the effective sample size is not discussed. On five paired folds, most nonparametric tests cannot reach p<0.006, and the p-values do not account for clustering by source slide. Please state the test, report exact p-values, and provide a justification for the sample size and independence assumptions.

minor comments (4)

[Equations (1)-(3)] Equations (1), (2), and (3) are rendered with placeholder '??' characters and are not readable; they need proper typesetting and complete definitions of all symbols.
[Section 3.3.2 and Section 3.4] The text refers to 'Table 6-3' and 'Figure 6-1,' which should be 'Table 3' and 'Figure 11,' respectively.
[Abstract and Section 1] The abstract contains 'This paper Proposes' with an uppercase 'P,' and the model acronym DSIR is not defined anywhere in the text; please define it and proofread for capitalization and grammar.
[Section 3.3.2] The narrative sentence 'Finally, adding the GAM module...' does not match the table structure, because the all-four-modules row already includes GAM and the final row adds data augmentation; please align the description with the rows in Table 3.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DSIR-YOLO is an empirical architecture comparison with standard metrics, not a derivation that reduces to its own inputs.

full rationale

The paper's central claims are measured object-detection results on a private p16/Ki-67 dual-stained cytology dataset. The DSIR-YOLO architecture is assembled from previously published components (Swin-Transformer, GAM attention, BiFPN-style multi-scale fusion, SPP, EIoU loss), each cited to independent prior work, and its performance is evaluated with standard precision, recall, and mAP metrics computed against the dataset's own annotations. There is no equation-level derivation in which a predicted quantity is defined in terms of the target quantity, no fitted parameter is renamed as a prediction, and no load-bearing uniqueness claim or ansatz is imported through self-citation; the reference list contains no self-citations by the present authors. The five-fold cross-validation and dataset-quality comparisons raise legitimate concerns about slide-level leakage and selection bias, but those are experimental-validity issues rather than circularity: the reported improvements are not forced by construction, by the definitions in the paper, or by a self-referential citation chain. The derivation chain is therefore self-contained as an empirical study, even though its generalization claims may be overstated.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claim rests on manually chosen training hyperparameters, correctness of expert annotations, and an independence assumption for patches. The most fragile premise is the patch-level split, which assumes independence across patches derived from only 11 slides. No new physical or conceptual entities are introduced.

free parameters (6)

Initial and minimum learning rate = 0.01 to 0.0001
Training hyperparameters chosen by hand; they directly affect convergence and final mAP. No learning-rate tuning analysis is reported.
SGD weight decay = 0.0005
Regularization strength set manually; affects generalization and final performance.
NMS confidence and IoU thresholds = 0.001 confidence, 0.60 NMS IoU
Post-processing thresholds chosen by hand; they set the precision-recall tradeoff and therefore alter all reported metrics.
Training epochs = 100
Stopping criterion selected without reported early stopping or epoch-sweep analysis.
Train/validation split ratio = 8:2
Random patch-level split; this choice affects variance and the reported numbers.
Data augmentation recipe = Mosaic, MixUp, random horizontal flip
Augmentation choices are hand-selected; the final ablation row adds augmentation together with all modules, confounding attribution of gains.

assumptions (4)

domain assumption Ground-truth labels produced under medical expert guidance are correct and consistent.
All evaluation metrics are computed relative to these labels; errors or inter-observer inconsistency would shift every reported number. Section 2.2 discusses annotation guidance.
ad hoc to paper Patches cropped from the same whole-slide image can be treated as independent samples for random splitting.
The paper splits 7,662 patches from only 11 source images randomly into 8:2 and five-fold sets; it never partitions by slide or patient, so train and validation may contain adjacent patches from the same slide. Sections 2.1 and 3.3.4.
domain assumption The implemented YOLOv5 and added modules faithfully reproduce the cited architectures.
No code or configuration is provided; the claimed baseline and module effects rest on the authors' implementation being correct. Sections 1 and 3.
domain assumption p16/Ki-67 dual-positivity cytological criteria are applied correctly.
The task is defined by these staining criteria; classification of positive versus negative cells depends on this medical definition. Sections 0 and 3.4.

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Cite this review

Pith. "Pith review of Research on Cervical Cancer p16/Ki-67 Immunohistochemical Dual-Staining Image Recognition Algorithm Based on YOLO." pith.science (2026). https://pith.science/paper/ANISUOT6

@misc{pith2026241201372,
  author       = {Pith},
  title        = {Pith review of: Research on Cervical Cancer p16/Ki-67 Immunohistochemical Dual-Staining Image Recognition Algorithm Based on YOLO},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ANISUOT6}},
  note         = {Machine review of arXiv:2412.01372}
}

read the original abstract

The p16/Ki-67 dual staining method is a new approach for cervical cancer screening with high sensitivity and specificity. However, there are issues of mis-detection and inaccurate recognition when the YOLOv5s algorithm is directly applied to dual-stained cell images. This paper Proposes a novel cervical cancer dual-stained image recognition (DSIR-YOLO) model based on an YOLOv5. By fusing the Swin-Transformer module, GAM attention mechanism, multi-scale feature fusion, and EIoU loss function, the detection performance is significantly improved, with mAP@0.5 and mAP@0.5:0.95 reaching 92.6% and 70.5%, respectively. Compared with YOLOv5s in five-fold cross-validation, the accuracy, recall, mAP@0.5, and mAP@0.5:0.95 of the improved algorithm are increased by 2.3%, 4.1%, 4.3%, and 8.0%, respectively, with smaller variances and higher stability. Compared with other detection algorithms, DSIR-YOLO in this paper sacrifices some performance requirements to improve the network recognition effect. In addition, the influence of dataset quality on the detection results is studied. By controlling the sealing property of pixels, scale difference, unlabelled cells, and diagonal annotation, the model detection accuracy, recall, mAP@0.5, and mAP@0.5:0.95 are improved by 13.3%, 15.3%, 18.3%, and 30.5%, respectively.

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

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