Diffusion-Based Approaches in Medical Image Generation and Analysis

Abdullah Al Nomaan Nafi; Md. Alamgir Hossain; Md Mahabub Uz Zaman; MD Manjurul Ahsan; Rakib Hossain Rifat; Shivakumar Raman

REVIEW 4 major objections 8 minor 62 references

Diffusion-Based Approaches in Medical Image Generation and Analysis

T0 review · 4 major / 8 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Trained only on diffusion-generated images, CNNs classify real medical images at 78–91% accuracy across three domains.

desk verdict Plausible numbers, untested headline: no CNN is ever trained on real data, and the synthetic labels' provenance is never established. read the letter →

arxiv 2412.16860 v1 pith:NJ4JIWDP submitted 2024-12-22 eess.IV cs.CV

Abdullah al Nomaan Nafi , Md. Alamgir Hossain , Rakib Hossain Rifat , Md Mahabub Uz Zaman , Md Manjurul Ahsan , Shivakumar Raman This is my paper · ORCID

classification eess.IVcs.CV

keywords diffusionmodelssyntheticmedicalimagesCNNtrainingondatascarcitybraintumorMRIleukemiaCOVID-19CTexplainableAI

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 asks whether synthetic medical images produced by a diffusion model can replace real, patient-specific images when training convolutional neural networks (CNNs) for diagnosis. It trains a diffusion model on a 20% sample of each of three public datasets—brain tumor MRI, leukemia blood smears, and COVID-19 CT scans—generates thousands of synthetic images per class, and then trains eight pretrained CNN architectures on those synthetic images alone. Evaluated on the unseen 80% of the original real images, the best models reach 78.24% accuracy on COVID CT, 86.46% on brain tumor MRI, and 91.38% on leukemia. The paper's point is that if this holds, patient-specific data may no longer be needed for CNN training in these tasks, easing privacy and scarcity constraints.

What carries the argument

The load-bearing object is the denoising diffusion probabilistic model (DDPM), trained by adding Gaussian noise to images in a forward Markov chain and learning to reverse that noising, so new images can be sampled from random noise. The paper trains one such model per domain on a stratified 20% sample, then uses the trained reverse process to generate large synthetic datasets with fixed per-class counts. Those synthetic datasets feed eight pretrained CNN architectures under 5-fold cross-validation, and LIME is applied to the top model in each domain to show which image regions drove predictions. The DDPM supplies the realism of the training data; the CNN supplies the classification; LIME supplies the interpretability.

What would settle it

Take the trained diffusion model, generate synthetic images for each declared class, and check whether images from the same class are more similar to each other than to images from other classes; if the classes are not separable, the reported CNN accuracies cannot be caused by class-specific content.

Watch

Extended reading notes

Core claim

The central claim is that a diffusion model can generate synthetic medical images statistically similar enough to real ones that CNNs trained exclusively on them classify real, unseen images at useful accuracy. On the three test domains, no real training images were used: the synthetic sets were built by sampling 1000–1700 images per class from the trained diffusion model, and CNN training, validation, and early stopping all used synthetic data. The best per-domain results are attributed to ResNet-50 for COVID CT, VGG-19 for brain tumor MRI, and DenseNet-121 for leukemia, with precision, recall, and F1-score in the same range. LIME heatmaps are offered as evidence that the models attend to disease-relevant regions rather than to artifacts. The paper presents the outcome as demonstrating potential, not as a finished clinical solution.

Load-bearing premise

The experiment assumes every synthetic image carries the correct disease label, but the paper never explains how labels are assigned and says it does not use a conditional diffusion model.

Editorial extensions

If this is right

If the central claim is correct, medical image classification pipelines can be built with zero real training images, leaving real data only for final evaluation.
Synthetic data could be shared openly without exposing patient-level information, since generated images are not recordings of any individual.
The 91.38% leukemia result suggests diffusion-generated data may be most useful where class differences are visually well defined, such as blood cell morphology.
Architecture choice matters on synthetic data: the best model differed by domain, so a single default CNN is not the right recommendation.
A practical route opens for rare diseases: a small sample of available images could be expanded into a large, balanced training set, mitigating class imbalance.

Reading between the lines

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

My inference: the comparative claim that synthetic training matches real-data training is not tested in this paper, since no CNNs trained on the original real images are reported as a baseline.
My inference: a direct next step would be training identical CNN architectures on the original 80% real data and on synthetic data, then comparing test accuracy and LIME maps to see whether synthetic-only training truly closes the gap.
My inference: because the paper says it does not use a conditional diffusion model yet later says the architecture leverages labels, the most direct extension is to repeat the pipeline with an explicitly label-conditioned DDPM and verify that generated images track their declared classes.
My inference: if verified, the approach could extend beyond classification to segmentation and anomaly detection, but those tasks require pixel-level label fidelity that the current study does not evaluate.

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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,

Here's the short version of arXiv:2412.16860: it reports credible-looking accuracy numbers for CNNs trained on diffusion-generated synthetic medical images, but the paper's advertised claim—that synthetic-trained CNNs compare to real-data-trained CNNs—is never tested. No CNN is ever trained on the original data. The authors list that comparison as future work in §5.6. The central conclusion is therefore unsupported.

What the paper does well: it uses three public datasets, eight pretrained CNN architectures, a stratified 20% sample for diffusion training, per-class synthetic generation, and evaluation on an untouched 80% real holdout. That evaluation is not circular. The LIME analysis on the best models is a reasonable extra. The literature review is broad and correctly situates the work against GANs and earlier diffusion results. So the pipeline is legitimate; the execution is another matter.

Soft spots, in proportion. The most serious is label provenance. §4.3 says the study "does not utilize a conditional diffusion model (CDM)," then two paragraphs later says the architecture "leverages the information from labeled data" and is "conditioned on the provided labels." §4.4 hedges that labels are inherited "if the DM architecture incorporates label conditioning." These cannot all be true. Table 1 reports exact per-class generation counts, but no protocol explains how a class label is assigned during sampling. If the model was unconditional, the CNN training signal is built on labels of unknown provenance. If a conditional model or per-class models were used, that implementation is missing from the text and from any released code or data. This is a load-bearing gap, not a typo.

Second, even with perfect labels, the headline comparison is untested. No model is trained on the original data, so Table 2's numbers—ResNet-50 at 78.24% on COVID CT, DenseNet-121 at 91.38% on leukemia—are absolute accuracies, not evidence of comparability. Third, the manuscript is unfinished: §4.3 literally contains a placeholder "[insert specific architecture reference here]". There are corrupted equations (Eq. 3, 10) and no code or data release. Fourth, the numbers have no confidence intervals, and the best architecture per dataset is selected post-hoc from eight candidates, which biases the top-line results upward.

My verdict: this is a useful cautionary example of evaluation design, not a publishable paper. It needs an answer to the conditioning contradiction, a real-data baseline, and a completed manuscript. I would not cite it. My recommendation to an editor: desk-reject or return for major revision; do not accept as-is. A serious workshop reader might find it useful as a discussion piece about what synthetic-data training claims actually require.

Best.

Referee Report

4 major / 8 minor

Summary. The paper investigates whether synthetic medical images generated by a diffusion model can be used to train CNN classifiers for three domains: brain tumor MRI, acute lymphoblastic leukemia microscopy, and SARS-CoV-2 CT scans. A diffusion model is trained on a stratified 20% sample of each real dataset; synthetic datasets of 1,000–1,700 images per class are generated; eight CNN architectures are trained on the synthetic data and evaluated on the untouched 80% real split. The authors report test accuracies up to 78.24%, 86.46%, and 91.38%, respectively, and present LIME explanations for the best models. The central claim, stated in the abstract, is that diffusion-generated samples can help CNNs perform comparably to models trained on original datasets, thereby reducing reliance on patient-specific data. As detailed below, this comparative claim is not tested by the experiments, and the label-assignment protocol for the synthetic images is internally inconsistent.

Significance. If the results were properly supported, the paper would provide a useful proof-of-concept that diffusion-model-synthesized medical images can carry enough class-discriminative information to train CNNs, with an evaluation against a real external holdout that is not circular. The use of multiple medical domains, multiple architectures, and a real-image test set are genuine strengths. However, the paper does not establish the advertised comparison with models trained on original data, and the provenance of the synthetic labels—a precondition for interpreting any of the reported accuracies—is not defined. These are not presentation issues; they are load-bearing gaps in the experimental design. The paper also does not release code, data, or trained models, and it contains unresolved placeholders, so the experiments cannot be independently checked. The significance of the contribution therefore cannot be assessed at this stage.

major comments (4)

[§4.3, §4.4, Table 1]
[Abstract, §5.4, §5.6]
[§4.5, §5.4]
[§4.5, §4.6, Table 2]

minor comments (8)

[§4.3]
[Eq. (10), Eq. (11)]
[Eq. (14)]
[§5.4]
[§4.7]
[§5.3, Table 1]
[References]
[§4.1, Data Availability]

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; the central evaluation uses an external real-image holdout, so the accuracy results are not constructed from their inputs.

full rationale

The paper's central claim is that CNN models trained on diffusion-generated synthetic medical images can classify unseen real medical images. That claim is tested against an untouched 80% split of the original real datasets (Section 4.6), so the reported test accuracies are measured on data external to the training pipeline and are not fitted parameters relabeled as predictions. No equation in the paper defines a target quantity in terms of itself, and no load-bearing result is imported from the authors' own prior work; in fact, the reference list contains no self-citations by the author team. The abstract's comparison to models trained on original datasets is not actually run, and the provenance of the synthetic-image labels is underspecified (Sections 4.3 and 4.4 first deny and then assert label conditioning), but these are reproducibility and validity gaps rather than circular reasoning. Because the evaluation benchmark is genuinely external and no derived quantity reduces by construction to an input, the circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claims rest primarily on domain assumptions about data representativeness and label correctness. The paper introduces no new theoretical entities. Several important parameters (diffusion and CNN hyperparameters) are not reported, so the number of hidden free parameters is effectively large.

free parameters (5)

Stratified sample fraction = 0.20
Chosen by hand in Section 4.2; the diffusion model is trained on only 20% of each dataset, and the paper does not test how this fraction affects results.
Synthetic dataset size per class = 1700 (MRI), 1000 (ALL), 1500 (CT)
Ad hoc choice reported in Table 1; no analysis of how generated dataset scale affects classifier accuracy.
CNN training hyperparameters = not reported
Learning rate, batch size, weight decay, and optimizer schedules are not specified; only the optimizer name (AdamW) and 50 max epochs are given in Section 4.5.
Diffusion model hyperparameters = not reported
Noise schedule, number of diffusion steps T, network architecture, and training configuration are not specified; the architecture is literally a placeholder in Section 4.3.
Early stopping patience = not reported
Section 4.5 says training stops when validation loss does not improve for a 'predefined number of epochs' but does not state the number.

assumptions (4)

domain assumption The diffusion model trained on a 20% stratified sample captures the full class-conditional image distribution.
Section 4.2 uses 20% sampling for tractability, and Section 5.3 states the generated dataset's size and characteristics 'will be further explored in future work,' so this representativeness is assumed, not validated.
domain assumption Synthetic images are assigned the correct class labels.
Section 4.3 first says the study does not use a conditional diffusion model, then says the architecture leverages labeled data; the labeling mechanism for generated images is never described, yet Table 1 reports per-class generated counts.
domain assumption ImageNet-pretrained weights (if used) provide a useful inductive bias for medical images.
The text calls the architectures 'pre-trained' in Section 5.4 but does not state whether weights are frozen, fine-tuned, or trained from scratch, or why transfer is expected to work for MRI, CT, and microscopy.
standard math DDPM theory as presented by Ho et al. is correct.
Section 3.3 restates the standard DDPM objective; this is background math the paper relies on without proof.

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Cite this review

Pith. "Pith review of Diffusion-Based Approaches in Medical Image Generation and Analysis." pith.science (2026). https://pith.science/paper/NJ4JIWDP

@misc{pith2026241216860,
  author       = {Pith},
  title        = {Pith review of: Diffusion-Based Approaches in Medical Image Generation and Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NJ4JIWDP}},
  note         = {Machine review of arXiv:2412.16860}
}

read the original abstract

Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, offer a potential solution by generating synthetic and realistic data. However, questions remain about the performance of convolutional neural network (CNN) models on original and synthetic datasets. If diffusion-generated samples can help CNN models perform comparably to those trained on original datasets, reliance on patient-specific data for training CNNs might be reduced. In this study, we investigated the effectiveness of diffusion models for generating synthetic medical images to train CNNs in three domains: Brain Tumor MRI, Acute Lymphoblastic Leukemia (ALL), and SARS-CoV-2 CT scans. A diffusion model was trained to generate synthetic datasets for each domain. Pre-trained CNN architectures were then trained on these synthetic datasets and evaluated on unseen real data. All three datasets achieved promising classification performance using CNNs trained on synthetic data. Local Interpretable Model-Agnostic Explanations (LIME) analysis revealed that the models focused on relevant image features for classification. This study demonstrates the potential of diffusion models to generate synthetic medical images for training CNNs in medical image analysis.

Figures

Figures reproduced from arXiv: 2412.16860 by the authors.

**Figure 2.** Generative learning trilemma [59] and the reverse diffusion process. The forward diffusion process adds noise to the input data, gradually increasing the noise level until the data is transformed into pure Gaussian noise. This process systematically perturbs the structure of the data distribution. The reverse diffusion process, also known as denoising, is then applied to recover the original structure of the data fr… view at source ↗

**Figure 3.** Architecture of Diffusion Model [61] Diffusion Probabilistic Models (DDPMs) [60, 35] are an example of this type of model, as they use a variational inference approach to estimate the parameters of a diffusion process. 3.3 Denoising Diffusion Probabilistic Models (DDPMs) Forward Process. DDPM defines the forward diffusion process as a Markov Chain where Gaussian noise is added in successive steps to obtain a set of … view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Flow diagram of our proposed methodology. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Sample images from the datasets a. Brain Tumor MRI b. Acute Lymphoblastic Leukemia (ALL) c. SARS [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: Sample images from the generated datasets [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

**Figure 7.** Figure 7: Local Interpretable Model-Agnostic Explanations (LIME) [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

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