Privacy-Preserving Automated Rosacea Detection Based on Medically Inspired Region of Interest Selection

Chengjun Liu; Chengyu Yang; Rishik Reddy Yesgari

REVIEW 3 major objections 3 minor 15 references

Privacy-Preserving Automated Rosacea Detection Based on Medically Inspired Region of Interest Selection

T0 review · 3 major / 3 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read The paper claims that a fixed redness-informed mask, derived from synthetic training faces and applied to all images, both hides identity and improves automated rosacea detection, lifting recall from 0.34 to 0.82 on real test data with near

desk verdict A plausible masking idea undermined by internally inconsistent headline numbers—worth a referee, but not publishable as-is. read the letter →

arxiv 2509.09844 v1 pith:KJN7HYZU submitted 2025-09-11 cs.CV

Chengyu Yang , Rishik Reddy Yesgari , Chengjun Liu This is my paper

classification cs.CV

keywords rosaceadetectionprivacy-preservingregionofinterestredness-informedmasksyntheticdataResNet-18clinicalpriorerythema

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

The paper tries to show that a privacy-preserving mask built once from the mean red-channel intensities of synthetic rosacea-positive training faces can serve both goals at once: it removes identity-revealing features and improves detection accuracy. A ResNet-18 trained solely on masked synthetic images detects 44 of 50 real rosacea patients, missing only 6, while the same network on unmasked full-face images detects just 17 of 50. The method also keeps false alarms at 1 out of 150 healthy faces. If true, this would mean that combining a clinical prior (rosacea shows as central facial redness) with entirely synthetic training data can yield a practical, privacy-safe screening tool for telemedicine and large-scale screening.

What carries the argument

The redness-informed ROI mask: the mean RGB face of all rosacea-positive synthetic training images is computed, its red channel is extracted, and the top t% (tuned to 29%) of red-intense pixels are set to 1 in a binary mask; all other pixels are set to 0. The mask is then applied as x ⊙ m to every training and test image. This single fixed mask does the paper's heavy lifting: it selects the clinically relevant rosacea regions, removes eyes and mouth, and creates the privacy-preserving input for a ResNet-18 classifier, an 18-layer residual convolutional network.

What would settle it

Compute the mask from a held-out set of real rosacea patient images instead of the synthetic training set; if the top-29% red regions barely overlap the original synthetic-derived mask, or if using the alternative mask drops the masked-image recall back toward the 0.34 full-face baseline, the claim that a fixed synthetic-derived prior transfers to real faces is refuted.

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Extended reading notes

Core claim

The paper's central claim is that a fixed redness-informed binary mask, obtained by averaging the red channel over synthetic rosacea-positive faces and thresholding at the top 29% of pixel intensities, focuses the classifier on diagnostically relevant regions—cheeks, nasal bridge, forehead—and excludes the eyes and mouth. Applied element-wise to every input image, this mask preserves the redness cue that carries the diagnosis while discarding identity information. On a real test set of 50 rosacea images and 150 healthy faces, the masked-image model achieves recall of 0.82 (44/50) and near-perfect precision (one false positive), whereas the full-face baseline achieves recall of only 0.34 (17/

Load-bearing premise

The load-bearing premise is that the mean red-channel geometry of synthetic rosacea-positive faces—specifically, which pixels rank in the top 29% of redness—matches where real rosacea appears on real faces across skin tones and lighting.

Editorial extensions

If this is right

Masked synthetic training outperforms full-face training on real-world test data, improving recall from 0.34 to 0.82 while keeping near-perfect precision.
The fixed mask can be derived once from a small set of synthetic positives, with no manual pixel-level annotations, then reused across datasets.
Removing identity-revealing regions does not degrade diagnostic performance; it improves it by suppressing irrelevant background and identity features.
The approach offers a template for other diffuse facial dermatoses that have a known spatial distribution, such as acne or melasma, using the same red-channel ROI logic.

Reading between the lines

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

The privacy claim is asserted but not measured: the paper does not run a face-recognition or re-identification test on the masked images, so 'privacy-preserving' currently rests on the anatomical argument that eyes and mouth are removed; a quantitative identity-retention experiment would settle it.
The mask's transferability is the key generalization risk: since the mask is derived from synthetic positives (presumably uniform skin tones and frontal poses), real patients with darker skin or unusual lighting may have erythema falling outside the fixed red-hot regions, which would reduce the recall gain.
The threshold t=29% was picked using synthetic validation after only three training epochs to exaggerate between-threshold differences; the final model trains 30 epochs, so the selected threshold may not be the one that maximizes final-model performance on real data.
If the redness prior holds across populations, the same mask could be adapted to a lightweight mobile screening app where only masked pixels are transmitted, further reducing privacy exposure.

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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

Colleague,

The core idea here is simple and defensible: train ResNet-18 on synthetic rosacea images masked by a fixed redness-informed ROI, derived from the mean red channel of the positive training set, and test on real images. The mask excludes eyes/mouth, which is a neat way to address privacy while focusing on diagnostic regions. The baseline comparison (masked vs unmasked, same architecture and training) is fair, and the threshold selection on validation is standard hyperparameter choice, not circular. The authors also ship code and data, which is good practice.

The problem is that the paper cannot decide what the results are. Table I reports masked-method recall 0.82, precision 1.0, accuracy 0.955, F1 0.9011. Table II's confusion matrix (TP=44, FN=6, FP=1, TN=149) implies recall 0.88, precision 0.9778, accuracy 0.965, F1 ≈0.926. The prose in Section IV-C matches Table II ('misses only 6 out of 50 patients', precision 97.78%), while the conclusion repeats Table I ('recall from 0.34 to 0.82, maintaining perfect precision'). That's three different versions of the headline result. The reader cannot know which numbers are real, and the claimed 'significant improvement' could be 0.82 or 0.88 recall with precision 1.0 or 0.9778. This is not a minor typo; it voids the central empirical claim.

There are other soft spots, but they're secondary. The privacy-preservation claim is asserted without any measurement: Figure 5 shows a blurred face but no face-recognition experiment to prove identity is removed. The test set is small (50 real positives) and no error bars are given. Also, the sentence in Section IV-B says the threshold is selected for 'best overall performance for testing'—sloppy wording; it's validation, but it reads like test-set leakage. The threshold is chosen after only 3 training epochs and then the model is trained for 30; that's a legitimate but unusual choice.

Who should read this: anyone working on privacy-preserving dermatological AI with synthetic data. The idea is worth knowing, but the numbers need to be reconciled before the result is usable. I'd send it to peer review with a request for major revision: re-run or clarify the metrics, add an identity-protection evaluation (e.g., face recognition accuracy before/after masking), and correct the wording. If Table II turns out to be the real result, the method still looks promising; if Table I is real, it's still a gain but smaller than the prose claims. Either way, it's not publishable in its current numeric state.

Referee Report

3 major / 3 minor

Summary. The paper proposes a privacy-preserving automated rosacea detection pipeline. A fixed binary mask is derived from the mean red channel of synthetic rosacea-positive training images by thresholding the top t% red pixels; the mask is applied to all images so that only central-face regions (cheeks, nose, forehead) remain. A ResNet-18 is trained on masked synthetic images and evaluated on 50 real rosacea images from web sources and 150 CelebA non-rosacea images. The authors report that masked training outperforms unmasked training, with recall improving from about 0.34 to 0.82/0.88 (depending on where one reads), precision around 1.0/0.978, and accuracy around 0.955/0.965. The paper positions the work as privacy-preserving because the mask removes the eyes and mouth.

Significance. If the reported result holds, the paper would make a useful contribution: it trains entirely on synthetic data, uses a simple clinically inspired prior, and reports code and data links, which are strengths. The idea of a fixed redness-informed mask that simultaneously focuses on diagnostically relevant areas and removes identity cues is appealing for telemedicine and large-scale screening. However, the central experimental evidence is currently not reliable because the printed metrics for the proposed masked method are internally inconsistent, and the privacy-preservation claim is asserted without any quantitative identity-protection measurement. The strength of the contribution therefore rests on claims that need to be corrected and verified.

major comments (3)

[Section IV-C, Tables I and II] The central performance claim is not supported by the printed numbers because the masked-method metrics contradict each other. Table I gives accuracy 0.9550, recall 0.8200, precision 1.0000, F1 0.9011. Table II's confusion matrix (TP=44, FN=6, FP=1, TN=149) implies recall=44/50=0.88, precision=44/45≈0.9778, accuracy=193/200=0.965, and F1≈0.926. Section IV-C subsequently reports 97.78% precision and 6 missed patients, agreeing with Table II, while Section V reports recall 0.82 and perfect precision, agreeing with Table I. Since the superiority claim over the unmasked baseline rests on these metrics, the reader cannot determine which result is real. Please supply a single consistent set of metrics, ideally with the exact code and confusion matrices used to generate them.
[Sections III-B and IV-C (Fig. 5)] The privacy-preservation claim is not quantitatively established. The mask removes the eyes and mouth but retains large portions of the cheeks, nose, and forehead; partial-face regions can still support identity recognition. Figure 5 is illustrative, not a measurement. Without a face-identification/obfuscation experiment (e.g., the accuracy of a face matcher on masked versus original images) or a formal privacy metric, the 'privacy-preserving' contribution is unverified.
[Section IV-C] All conclusions are based on single-run point estimates on one test set (50 positive, 150 negative), with no error bars, confidence intervals, or multiple seeds. The reported recall for the masked method itself changes by 0.06 depending on the table (0.82 vs. 0.88), and the positive test set contains only 50 images. The robustness of the claimed improvement is therefore unclear. Please report variability across runs and, if possible, a statistical comparison between the masked and unmasked models.

minor comments (3)

[Algorithm 1] The threshold notation is ambiguous: line 6 uses `⌈t%⌉·w·h`, which is dimensionally inconsistent with t being a percentage; it should be `⌈(t/100)·w·h⌉`. Also, lines 7–12 modify the loop variable `i`; a separate output mask variable should be used.
[References / Related Work] Reference [1] is cited for augmenting liver lesion datasets, but the listed title is about skin lesions; please verify the citation and its description. Also, the OSF link is view-only; a permanent public link would help reproducibility.
[Typos] There are several typos: 'componet' in the Fig. 1 caption, 'shresholds' in Fig. 4, 'V alidation' in Section IV-A header, and 'toptpercent' in Section III-A. These should be corrected.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation; central claim is empirical. Self-citations only for data provenance. Internal metric inconsistencies and unmeasured privacy claim are correctness concerns, not circularity.

full rationale

The paper's derivation chain is empirical rather than definitional. The ROI mask is computed from the mean red channel of rosacea-positive synthetic training images (Algorithm 1), and the threshold t is selected by maximizing validation metrics over t in [20,35] (Section IV-B). This is standard hyperparameter selection on the validation set, not on the real test labels; the test set (50 real rosacea images, 150 CelebA negatives) is separate. The comparison between masked and unmasked training is an actual experiment, not an identity. No equation reduces a predicted quantity to an input: the mask is a preprocessing step, and the classifier output is learned from the masked inputs. The paper's self-citations [13] and [14] are used to source the synthetic dataset and prior rosacea-detection context; the current experiments are independently run and the data are linked on OSF, so the central claim does not rest solely on an unverified self-citation. No uniqueness theorem or ansatz is imported from the authors' prior work. Two non-circular concerns should be noted. First, the reported metrics are internally inconsistent: Table I gives accuracy 0.9550, recall 0.8200, precision 1.0000, F1 0.9011, while Table II's confusion matrix (TN=149, FP=1, FN=6, TP=44) implies accuracy 0.965, recall 0.88, precision 0.9778, F1 0.926; the prose and conclusion report yet another combination. This undermines the empirical support for the central claim but is not circularity. Second, the privacy-preserving claim is asserted via the mask's exclusion of low-red regions and a visual figure, without a face-recognition or identity-matching evaluation; that is missing evidence, not a circular argument.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The method introduces no new physical or mathematical entities. It relies on a threshold parameter and four domain assumptions about disease manifestation, color value, synthetic-to-real transfer, and identity masking.

free parameters (1)

Mask threshold t (top percentage of red pixels) = 29% (28% best on validation, raised by hand to 29% to balance performance with privacy)
Selected by scanning 20-35% on a 3-epoch validation run; the final value is a manual adjustment, not a principled derivation.

assumptions (4)

domain assumption Rosacea manifests primarily as central facial erythema (cheeks, nose, forehead).
Used in Section III-A to justify the ROI; cited from [2], but not verified in the paper.
domain assumption Red channel pixel intensity correlates with erythema in facial images.
The entire mask construction in Algorithm 1 is based on red channel intensity; no calibration to clinical erythema scoring is provided.
domain assumption The synthetic training distribution is close enough to real facial images for the model to transfer.
Training and validation use GAN/StyleGAN synthetic faces; testing uses real images; no domain-gap analysis is provided (Section IV-A).
domain assumption Pixels with low red intensity (eyes, mouth) are identity-revealing, and keeping the high-red regions removes identity.
Asserted in Section III-A and Figure 5; no quantitative identity-protection evaluation is reported.

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Cite this review

Pith. "Pith review of Privacy-Preserving Automated Rosacea Detection Based on Medically Inspired Region of Interest Selection." pith.science (2026). https://pith.science/paper/KJN7HYZU

@misc{pith2026250909844,
  author       = {Pith},
  title        = {Pith review of: Privacy-Preserving Automated Rosacea Detection Based on Medically Inspired Region of Interest Selection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KJN7HYZU}},
  note         = {Machine review of arXiv:2509.09844}
}

read the original abstract

Rosacea is a common but underdiagnosed inflammatory skin condition that primarily affects the central face and presents with subtle redness, pustules, and visible blood vessels. Automated detection remains challenging due to the diffuse nature of symptoms, the scarcity of labeled datasets, and privacy concerns associated with using identifiable facial images. A novel privacy-preserving automated rosacea detection method inspired by clinical priors and trained entirely on synthetic data is presented in this paper. Specifically, the proposed method, which leverages the observation that rosacea manifests predominantly through central facial erythema, first constructs a fixed redness-informed mask by selecting regions with consistently high red channel intensity across facial images. The mask thus is able to focus on diagnostically relevant areas such as the cheeks, nose, and forehead and exclude identity-revealing features. Second, the ResNet-18 deep learning method, which is trained on the masked synthetic images, achieves superior performance over the full-face baselines with notable gains in terms of accuracy, recall and F1 score when evaluated using the real-world test data. The experimental results demonstrate that the synthetic data and clinical priors can jointly enable accurate and ethical dermatological AI systems, especially for privacy sensitive applications in telemedicine and large-scale screening.

Figures

Figures reproduced from arXiv: 2509.09844 by the authors.

**Figure 2.** The system architecture of the proposed privacy-preserving automated [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** The first column shows the mean images of the two training datasets from the rosacea negative and positive classes, respectively. The remaining seven [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Validation performance for different shresholds [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Identity Protection Effect After Applying the Mask [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

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Reference graph

Works this paper leans on

15 extracted references · 1 linked inside Pith

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Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.

[1] [1]

Generating highly realistic images of skin lesions with GANs,

C. Baur, S. Albarqouni, and N. Navab, “Generating highly realistic images of skin lesions with GANs,” Springer, 2018, pp. 260–267

2018

[2] [2]

Ros-net: A deep convolutional neural network for automatic identification of rosacea lesions,

H. Binol, A. Plotner, J. Sopkovich, B. Kaffenberger, M. K. K. Niazi, and M. N. Gurcan, “Ros-net: A deep convolutional neural network for automatic identification of rosacea lesions,” Skin Research and Technology, V ol. 26, No. 3, 2020, pp. 413–421

2020

[3] [3]

Dermatologist-level classification of skin cancer with deep neural networks,

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, V ol. 542, No. 7639, 2017, pp. 115–118

2017

[4] [4]

Differential diagnosis of rosacea using machine learning and dermoscopy,

L. Ge, Y . Li, Y . Wu, Z. Fan, and Z. Song, “Differential diagnosis of rosacea using machine learning and dermoscopy,” Clinical, Cosmetic and Investigational Dermatology, 2022, pp. 1465–1473

2022

[5] [5]

Generative adversarial networks,

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y . Bengio, “Generative adversarial networks,” Communications of the ACM, V ol. 63, No. 11, 2020, pp. 139–144

2020

[6] [6]

Multi-label chest X-ray image classification via category-wise residual attention learning,

Q. Guan and Y . Huang, “Multi-label chest X-ray image classification via category-wise residual attention learning,” Pattern Recognition Letters, V ol. 130, 2020, pp. 259–266

2020

[7] [7]

Deep residual learning for image recognition,

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2016, pp. 770–778

2016

[8] [8]

A style-based generator architecture for generative adversarial networks,

T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2019, pp. 4401–4410

2019