Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection

Chung-Hwan Jung; Hyun-Woo Kim; In Kyu Lee; Jaesik Min; Sumin Seo

REVIEW 4 major objections 6 minor 24 references

Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Adding user-specified synthetic lesions to angiography training data improves coronary stenosis detection and severity classification beyond real-data-only training, on both an in-house and a public dataset.

desk verdict Sensible pipeline, honestly written, but the headline gains are small, unverified, and partly circular; worth reviewing carefully rather than rejecting. read the letter →

arxiv 2508.00438 v1 pith:V54NESYJ submitted 2025-08-01 eess.IV cs.CV

Sumin Seo , In Kyu Lee , Hyun-Woo Kim , Jaesik Min , Chung-Hwan Jung This is my paper · ORCID

classification eess.IVcs.CV

keywords coronarystenosisangiographydiffusionmodelsdataaugmentationinpaintinglesiondetectionseverityclassificationclassimbalance

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

Accurate detection and severity grading of coronary stenosis from angiography is held back by small, imbalanced datasets and expensive manual labels. This paper proposes DiGDA, a data-augmentation pipeline that uses a diffusion inpainting model, conditioned on user-modified vessel segmentation masks and lesion bounding boxes, to generate realistic synthetic lesions at a user-specified percentage diameter stenosis (%DS). The synthetic images are balanced across moderate and severe classes and added to the training set of a YOLO detection-and-classification model. On both a large in-house dataset and a public external dataset, the augmented model reports higher F1, precision, recall, and mAP50 than real-data-only training, with the largest gains concentrated in the severe class, and it stays ahead under data-scarce training subsets. The claim is that this form of targeted, controllable augmentation replaces some of the need for additional expert annotations.

What carries the argument

The load-bearing object is the multi-ControlNet inpainting stage: a latent diffusion model, based on Stable Diffusion, whose denoising network $\epsilon_\theta$ is fine-tuned with zero-convolution conditioning layers to take two extra inputs -- an edited vessel segmentation mask $c_s$ and a masked image with a lesion bounding box $c_m$ -- along with the text prompt $c_t$ and timestep $t$. The vessel mask is edited by a QCA-based algorithm that moves two control points at the minimum lumen diameter orthogonally to the vessel direction until the computed %DS, $\%DS = (1 - MLD/D_{\mathrm{ref}}) \times 100$, matches the user's target, with smooth propagation to neighboring contour points. These conditions let the model redraw only the stenosed segment while preserving the surrounding angiographic context. The downstream detector is a YOLO model that simultaneously outputs lesion bounding boxes and a moderate/severe class.

What would settle it

Take a sample of generated images, run the same QCA tool used to create the training labels on them, and compare the measured %DS with the user-specified target; if the measured values are systematically off or broadly scattered, the claim that severity control is the mechanism behind the gains is not supported.

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Extended reading notes

Core claim

The central claim is that training a single-stage lesion detector on a mix of real angiograms and diffusion-inpainted synthetic angiograms, generated with user-controlled stenosis severity, improves both localization and severity classification relative to training on real data alone. The paper reports mAP50 increasing from 0.688 with no synthetic data to 0.717 with a ×4 balanced synthetic set on the internal test set, and corresponding F1 and mAP50 improvements on the external public dataset. The method restricts generation to targeted lesion regions by inpainting within bounding boxes, using a multi-ControlNet that takes both the vessel segmentation mask (edited to a target %DS) and the masked original image as conditions. This is presented as the first use of inpainting-based diffusion augmentation for lesion detection in coronary angiography, and the authors argue it avoids the unintended lesion artifacts that full-image synthesis can introduce. The severity control is achieved by a QCA-based algorithm that adjusts the minimum lumen diameter in the vessel contour to match the requested %DS.

Load-bearing premise

The augmented images must really look like coronary angiograms with the stenosis severity the user asked for; if the detector learns from images that do not match their labels, the reported gains will not transfer to real clinical data.

Editorial extensions

If this is right

At a ×4 synthetic-to-real ratio, the internal mAP50 rises from 0.688 to 0.717, with the severe-lesion class improving from 0.646 to 0.663, so the augmentation most helps the underrepresented class.
The external public dataset shows the same trend (F1 from 0.500 to 0.513 at ×4), suggesting the benefit transfers across imaging protocols and patient populations.
A balanced synthetic set outperforms an imbalanced synthetic set of the same total size across model scales, so the class-balancing step is part of why the method works.
Under data-scarce subsets (as low as 5% of the training data), the augmented model maintains higher mAP50 than real-only training, particularly for severe lesions, indicating the method can stretch limited labeled collections.

Reading between the lines

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

One implication the paper leaves implicit: if the gains come mainly from adding diverse, balanced examples rather than from severity-accurate labels, then a cheaper generator that ignores target %DS and only adds controlled diversity might capture most of the benefit; a comparison against synthetic images with shuffled severity labels would settle this.
A natural extension is to apply the same mask-edit-and-inpaint recipe to other focal vascular lesions, such as aneurysms, dissections, or calcified plaques, where lesions can appear anywhere along a vessel and labeled examples are scarce.
The method's preservation of background context outside the edited bounding box makes it more suited to clinical trust than full-image synthesis, but that trust would still need a reader study where cardiologists rate generated frames for realism and severity before deployment.

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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

The core idea here is genuinely new and the paper is honestly written. The combination of user-guided vessel-mask modification with multi-ControlNet inpainting, targeting the lesion region rather than synthesizing whole images, is a reasonable step beyond prior diffusion augmentation work, which mostly targeted classification. The ablations on class balance, data scarcity, and model size are thoughtful, and the plan to release the re-annotated ARCADE data is a credit.

That said, the empirical support for the central claim is weaker than the prose suggests. The gains are small (internal mAP50 0.688 to 0.717; ARCADE F1 0.500 to 0.524), reported as averages over three runs with no error bars or significance tests, so we do not know whether these differences are real or noise. The ARCADE results are non-monotonic: ×4 underperforms ×2 on both F1 and mAP50, which sits uncomfortably with the claim of consistent superiority.

The load-bearing weakness is the label-verification problem. The synthetic images are labeled with a target %DS derived from the modified vessel mask, but the paper never checks whether the diffusion model actually renders that severity. Re-running QCA on generated images or asking clinicians to grade a sample would settle this. If the inpainting smooths or distorts the narrowing, the synthetic labels are systematically wrong and the downstream detector learns incorrect associations—which could easily produce or erase the modest gains reported.

The external validation is also partly circular. The same QCA tool that generates synthetic labels is used to re-annotate the ARCADE validation set. Even with clinician refinement, this aligns the label noise in synthetic training data and external evaluation, potentially biasing the comparison in the method's favor. A simpler baseline—like oversampling the minority class or standard geometric augmentation—would help calibrate whether the gains come from the diffusion synthesis specifically or just from rebalancing and more data.

Who is this for? Researchers working on diffusion-based augmentation for detection in medical imaging, and anyone thinking about synthetic label fidelity. The paper deserves a serious referee: the idea is coherent, the pipeline is reproducible in principle, and the claimed direction is plausible. A referee should ask for quantitative verification of the synthetic severity labels, significance tests, and at least one cheap augmentation baseline. With those additions the claims would be much more convincing.

Referee Report

4 major / 6 minor

Summary. The paper proposes DiGDA, a diffusion-based data augmentation pipeline for coronary stenosis detection. The method uses a QCA tool to extract vessel contours and lesion locations from real angiograms, lets a user modify the vessel mask to a target percentage diameter stenosis (%DS), and trains a multi-ControlNet diffusion model to inpaint the lesion region so that the generated image reflects the requested severity. Synthetic images are then added to the training set at ×1, ×2, and ×4 ratios with a class-balanced selection, and a YOLO detector is trained for joint lesion detection and severity classification (moderate vs severe). Experiments on a large internal dataset and on the public ARCADE dataset report higher F1 and mAP50 than a real-data-only baseline, along with ablations on class balance and data scarcity.

Significance. If the reported results are reliable, the paper offers a practical solution to two persistent problems in coronary angiography analysis: limited labeled data and class imbalance. The targeted inpainting strategy is a reasonable extension of ControlNet-based medical image synthesis, and the promise to release a re-annotated ARCADE validation set is a useful contribution to the community. The main value would lie in showing that user-controlled severity modification can inject clinically meaningful variation into training data. However, the central claim rests on an unverified assumption about the faithfulness of the generated images to the specified %DS, and the statistical evidence for the quantitative gains is currently thin; the significance of the work therefore depends on whether these limitations can be addressed with additional experiments.

major comments (4)

[§2.2, §2.3, Fig. 1] The synthetic labels are assigned from the user-specified target %DS that is applied to the vessel mask, but the paper never verifies that the generated angiogram actually exhibits that stenosis severity. Since the downstream YOLO detector is trained to associate image content with these labels, a systematic mismatch between target and rendered %DS could corrupt the training signal and explain part or all of the reported gains. The authors should re-apply their QCA pipeline to a sample of generated images and report the distribution of target vs measured %DS, or provide a clinician reader study grading the generated severity. Without such verification, the central improvement claim is not fully supported.
[§3.3, Table 2] All downstream results are averages over three runs with no standard deviations, confidence intervals, or significance tests. The phrase "significant lesion detection performance gain" for the internal ×4 setting (mAP50 0.717 vs 0.688) is therefore not statistically substantiated. Moreover, the ARCADE results contradict the stated monotonic improvement: the ×4 setting (F1 0.513, mAP50 0.492) performs worse than ×2 (F1 0.524, mAP50 0.501). The authors should add variance information, perform paired tests or confidence intervals, and explicitly discuss the non-monotonic external behavior.
[§3.3, Fig. 4(b)] The data-scarcity comparison between "Real-only" and "Ours" does not control for the total number of training images: the "Ours" condition adds synthetic data on top of the real subset, so the model receives more training examples. The observed mAP50 gains could therefore reflect increased data quantity rather than the specific usefulness of the generated synthetic images. To support the claim that the method "maintains high ... performance even when trained with limited data," the authors should include a control condition that matches total data size, for example by training on a larger real subset of equal size or on real data with standard augmentation.
[§3.1, §3.3] The external validation on ARCADE is based on a re-annotation of the validation set with the same in-house QCA tool that is used to annotate the internal real data and to define the synthetic stenosis labels. This creates a consistency between training and evaluation that is not an independent clinical gold standard. The paper should explicitly acknowledge that ARCADE performance measures agreement with the QCA-based labeling pipeline, and ideally compare against expert clinician grading on a subset to assess clinical validity.

minor comments (6)

[§2.2, Eq. (1)] The expectation in the loss is written as "E[∥ϵ − ϵθ(zt,t,ct,cs,cm)∥2]" but the sampling notation "t, ct, cs, cm ∼ N(0,1)" is confusing because the noise ϵ is the only variable drawn from a standard normal; the subscript structure of the expectation should be clarified.
[§3.2] The paper states that average scores over three runs are reported, but no standard deviations or variance information appear in Table 2 or Figure 4; the figures would be more informative with error bars or interval shading.
[§1] The claim that this is "the first to integrate an inpainting strategy with a generative model that synthesizes angiograms for lesion detection" is strong, and the related-work section covers only a few diffusion-based augmentation papers; a broader search might reveal closer prior work on coronary angiography generation.
[§3.1] The internal dataset is described only as a "curated collection" of angiograms; acquisition protocol details, patient selection criteria, and exclusion criteria would improve reproducibility and allow readers to judge the clinical scope.
[§3.4, Fig. 1] The qualitative examples show original and generated images but do not overlay the modified vessel masks or the intended MLD location on the generated images; showing these overlays would help the reader assess the correspondence between the requested severity change and the visible output.
[§2.2] The text says "convolution network" where "convolutional network" would be more standard, and there is a typo in the Table 1 header "V alid"; these small issues should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DiGDA's augmentation claim rests on external benchmarks, not on self-citation or fitted-input prediction.

full rationale

The paper's central claim is an empirical benchmark result: adding synthetic images generated by a diffusion-based inpainting pipeline improves lesion detection and severity classification on an internal held-out test set and on the public ARCADE dataset. No equation in the paper defines the predicted quantity in terms of the input that is then said to predict it. The %DS labels for synthetic images are user-specified targets used to condition the ControlNet, while the reported gains are measured on real images labeled by QCA and clinician verification. The ARCADE validation set is re-annotated with the same QCA tool, which creates a labeling-consistency concern for external validity, but it does not make the detection result a mathematical consequence of the synthesis procedure; the detector must still generalize to real images. The only substantive unverified assumption is that the diffusion model faithfully renders the modified vessel masks into images whose true stenosis severity matches the target %DS. That is a correctness and interpretability limitation, not a circularity: it is an empirical claim about image realism that the paper supports only qualitatively, but it is not equivalent to the inputs by construction. There are no load-bearing self-citations, no imported uniqueness theorems, and no fitted parameter renamed as a prediction. Accordingly, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new mathematical axioms or invented entities. The assumptions listed are domain-specific premises about the accuracy of QCA measurements, the realism of the diffusion-generated images, and the transferability of synthetic data to real data. These are necessary for the central claim to hold.

assumptions (3)

domain assumption The QCA-derived vessel contours and reference diameter estimates accurately reflect true vessel geometry.
The synthetic data generation modifies vessel masks based on QCA estimates of MLD and Dref (Section 2.1). If these estimates are inaccurate, the generated images will not have the intended %DS.
domain assumption The diffusion inpainting model can generate images that follow the modified vessel mask and preserve the rest of the angiogram realistically.
The method relies on ControlNet to synthesize lesions that are anatomically plausible and consistent with the target severity. Only qualitative examples are provided, with no quantitative validation.
domain assumption The YOLO detector trained on real plus synthetic data generalizes to real test data.
This is the standard premise of data augmentation: synthetic data must transfer to real-world performance. The paper's experiments test this, but the small gains and lack of statistical support leave this assumption partially unverified.

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Cite this review

Pith. "Pith review of Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection." pith.science (2026). https://pith.science/paper/V54NESYJ

@misc{pith2026250800438,
  author       = {Pith},
  title        = {Pith review of: Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V54NESYJ}},
  note         = {Machine review of arXiv:2508.00438}
}

read the original abstract

Coronary stenosis is a major risk factor for ischemic heart events leading to increased mortality, and medical treatments for this condition require meticulous, labor-intensive analysis. Coronary angiography provides critical visual cues for assessing stenosis, supporting clinicians in making informed decisions for diagnosis and treatment. Recent advances in deep learning have shown great potential for automated localization and severity measurement of stenosis. In real-world scenarios, however, the success of these competent approaches is often hindered by challenges such as limited labeled data and class imbalance. In this study, we propose a novel data augmentation approach that uses an inpainting method based on a diffusion model to generate realistic lesions, allowing user-guided control of severity. Extensive evaluation on lesion detection and severity classification across various synthetic dataset sizes shows superior performance of our method on both a large-scale in-house dataset and a public coronary angiography dataset. Furthermore, our approach maintains high detection and classification performance even when trained with limited data, highlighting its clinical importance in improving the assessment of severity of stenosis and optimizing data utilization for more reliable decision support.

Figures

Figures reproduced from arXiv: 2508.00438 by the authors.

**Figure 1.** Examples of coronary angiograms generated with various levels of stenosis. (a,c,e) are the original angiograms and (b,d,f) are the angiograms generated with specific %DS values applied to the vessel segmentation maps. (g) describes how the automatic algorithm generates a mask when numeric value of %DS is given by user. Minimum lumen diameter (MLD) point is marked as a red circle in the image and represents the cente… view at source ↗

**Figure 2.** Overall pipeline of proposed method. (1,2) We prepare conditioning images and generate prompts from metadata. (3) ControlNet is trained to reconstruct images and (4,5) inferred to generate new images with modified segmentation maps. (6) YOLO model captures lesions and predicts their severity. problems in other medical imaging domains, lesions in angiograms can occur at any location within the vessels, making it im… view at source ↗

**Figure 3.** Label distributions. 3.1 Datasets Internal dataset is composed of 7,894 X-ray coronary angiograms (train 5,156, validation 1,854, test 884 images), which is a curated collection of high-resolution [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Performance comparisons on lesion detection across data compositions and dataset sizes. (a) examines the effect of class balance: the balanced set has equal target lesions per class, while the imbalanced set maintains the original distribution with the same amount of…

Discussion (0). Continue with ORCID to comment.

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Reviewed August 6, 2026 · model on record in the stance chip above.

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