Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review

Abdullah; Euijoon Ahn; Ickjai Lee; Tao Huang

REVIEW 5 major objections 6 minor 212 references

Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review

T0 review · 5 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This survey claims that no prior review has compared diffusion models on latency for gigapixel or 3D medical images, and positions WDM and LDM as the efficient answers.

desk verdict A survey with a useful categorization that is undermined by a false uniqueness claim, incomparable benchmark data, and corrupted equations; not ready for peer review. read the letter →

arxiv 2505.07866 v1 pith:DHQA5IL2 submitted 2025-05-09 eess.IV cs.AIcs.CV

Abdullah , Tao Huang , Ickjai Lee , Euijoon Ahn This is my paper · ORCID

classification eess.IVcs.AIcs.CV

keywords diffusionmodelsmedicalimaginglatentmodelwaveletinferencetimecomputationalefficiencygenerativesurvey

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 review claims that no earlier survey has directly compared diffusion models on latency and time complexity for extremely large medical images such as whole-slide histopathology and 3D MRI/CT. To fill that gap, it organizes the field into three model families — DDPM, LDM, and WDM — and compares them on image quality, stability, convergence, speed, and scalability. The survey's practical conclusion is that DDPM gives the highest quality but slowest sampling, LDM balances quality and speed by working in a compressed latent space, and WDM is fastest by diffusing in the wavelet domain. It matters because medical imaging needs fast, reliable generation at very high resolutions, where inference time is a real bottleneck. The paper argues that model selection should be application-specific rather than a single universal choice.

What carries the argument

The organizing machinery is a three-way taxonomy — DDPM, LDM, WDM — mapped against the generative learning trilemma (sample quality, sampling speed, and mode coverage/diversity), with comparisons carried by quality metrics such as Fréchet Inception Distance (FID), parameter counts, and wall-clock inference times. Within the taxonomy, the load-bearing mechanisms are the LDM's pre-trained autoencoder that shifts diffusion from pixel space to latent space, and the WDM's discrete wavelet transform that decomposes an image into four quarter-resolution subbands, reducing the diffusion computation by a factor of four. These two mechanisms are what the survey credits with closing the efficiency gap left by DDPM's step-by-step denoising.

What would settle it

Run DDPM, LDM, and WDM on the same machine and the same dataset (for example, CIFAR-10 or CelebA-HQ), measure FID and wall-clock sampling time with identical hardware and hyperparameters, and check whether WDM remains the fastest and LDM the balanced middle ground from the table.

Watch

Extended reading notes

Core claim

The paper's central claim is that a gap exists in the diffusion-model literature: existing surveys cover quality, applications, and general computational cost, but none specifically discusses latency and time complexity for extremely large high-resolution data such as whole-slide images or 3D MRI/CT. On the paper's own terms, filling that gap yields a consistent quality–speed–scalability ranking: DDPM leads in fidelity at the cost of slow iterative sampling (e.g., 80.5 seconds on CIFAR-10), LDM operates in the latent space of a pre-trained autoencoder to reach a middle ground, and WDM decomposes images with a discrete wavelet transform, cutting dimensionality fourfold and delivering the fastest reported times. This ordering supports the review's claim that WDM and LDM address the diffusion trilemma, and that applications requiring fine detail at speed — whole-slide pathology, 3D CT/MRI — are exactly where the choice of model matters most.

Load-bearing premise

The recommendation that LDM is balanced and WDM is fast depends on comparing FID and inference times reported in separate papers, on different datasets, with no shared hardware or error bars; if those numbers are not directly comparable, the ranking loses its support.

Editorial extensions

If this is right

For tasks where generation quality dominates and time is not critical, DDPM remains the strongest choice; on CIFAR-10 it reaches the best FID (3.21) but takes about 80.5 seconds.
For applications that need both quality and speed, LDM is the recommended middle ground, achieving competitive FID (4.98 on FFHQ) at about 0.43 seconds.
For speed-critical, detail-heavy cases such as whole-slide pathology, WDM is positioned as the fastest option (0.08 seconds on CIFAR-10 with FID 4.01) and best at preserving texture.
The survey claims to fill a gap no existing survey covers: latency and time complexity for extremely large high-resolution data such as whole-slide images and 3D MRI/CT.
Model selection should be application-specific rather than a single universal best model.

Reading between the lines

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

Beyond the paper: a standardized benchmark that runs DDPM, LDM, and WDM on the same hardware, same dataset, and same resolution (ideally whole-slide images or 3D MRI/CT) would turn the survey's cross-paper numbers into a testable ranking.
Beyond the paper: because LDM loses fine details and WDM is moderate in sampling efficiency, a hybrid that first compresses into a latent space and then diffuses in the wavelet domain is a natural candidate for pushing both speed and texture fidelity.
Beyond the paper: for real-time clinical deployment, the survey's own ranking implies that DDPM would be used offline while WDM or distilled one-step variants are the route to interactive latency, an extension the review does not explore.
Beyond the paper: the reported gap also suggests that future medical-imaging surveys should report hardware, measurement protocol, and error bars so latency claims become reproducible rather than aggregated.

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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

You asked about the diffusion-model survey (arXiv:2505.07866). My take: the useful core is a compact categorization of DDPM, LDM, and WDM with a decent map of recent applications, especially in medical imaging. The application tables (Tables 4-9) and Fig. 5 give a quick entry point to the literature. If someone wants names and citations for wavelet-based diffusion in medical imaging, this is a reasonable starting place.

The soft spots are not minor. The central uniqueness claim in Section 1—"no existing studies specifically discuss the latency and time complexity of generative models, specifically dealing with extremely large high-resolution data such as WSI or 3D MRI/CT"—is contradicted by reference [11], an efficient-diffusion survey they themselves cite and mark in Table 1 as covering efficient sampling and fast convergence. That alone would be a problem, but the survey also never delivers the promised latency analysis for WSI or 3D MRI/CT. Table 3 compares FID and inference times across different datasets (CIFAR-10, FFHQ, CelebA-HQ, LSUN-Church) with no hardware, measurement protocol, or error bars, so the Section 4 model-selection recommendations rest on numbers that are not comparable. Eq. (5) is garbled—it does not resemble a recognizable ELBO/kl term—and the conclusion cites [36] as the DDPM foundation when that reference is RePaint; the actual DDPM paper is [15]. These are not cosmetic typos; they affect whether the survey can be trusted as a map.

That said, the paper is not useless. As an orientation for someone new to the area, the three-way taxonomy and the medical applications survey have some value. But a review's value is its accuracy, and the inaccuracies here are load-bearing: the gap claim is false, the quantitative comparison is apples-to-oranges, and the equations are corrupted. I would not send this to a serious referee in its current form. It needs a major overhaul: fix the equations, correct the citations, rework or substantiate the uniqueness claim, and replace Table 3 with a controlled comparison or a clearly labeled reporting of published numbers with dataset and hardware context.

My recommendation: desk reject, but invite resubmission if the authors address these issues. Not worth reading-group time until then.

Referee Report

5 major / 6 minor

Summary. The manuscript is a survey of diffusion-based generative models in medical imaging, organized around three model families: Denoising Diffusion Probabilistic Models (DDPM), Latent Diffusion Models (LDM), and Wavelet Diffusion Models (WDM). It claims to be the first survey to analyze latency and time complexity for extremely large high-resolution medical data such as whole slide images and 3D MRI/CT, and it promises a comparative analysis of generative quality and computational cost. The paper provides an overview of the three frameworks, applies them to natural and medical imaging tasks (generation, translation, reconstruction, segmentation, classification, editing, denoising, registration), and concludes with model-selection guidance: DDPM for maximum quality at low speed, LDM for a quality/speed balance, and WDM for fast inference with detail preservation.

Significance. The topic is timely, and the organizational effort is a genuine service to the community: the paper compiles a large number of recent applications into a structured taxonomy (Figure 5; Tables 4-9) and identifies three efficiency-relevant model families. This bibliography alone could be useful to a newcomer. However, the paper ships no reproducible experiments, no machine-checked proofs, and the quantitative comparisons are not derived from a unified protocol. More importantly, the central positioning claim—that no prior survey discusses latency for WSI/3D MRI/CT and that this survey fills that gap—is contradicted by the manuscript's own cited prior survey [11], and the promised latency analysis for large medical data is not actually delivered. As a review, the paper's value depends on the reliability of its comparative statements; the issues identified below compromise that reliability in load-bearing places.

major comments (5)

[Section 1 and Table 3] The central gap statement—'no existing studies specifically discuss the latency and time complexity of generative models, specifically dealing with extremely large high-resolution data such as WSI or 3D MRI/CT'—is contradicted by the paper's own Table 1, which cites [11] 'Efficient Diffusion Models for Vision: A Survey' and marks that prior work as covering efficiency, efficient sampling, and fast convergence. Moreover, the promised gap-filling content is not present in the manuscript: Table 3 evaluates natural-image benchmarks only (CIFAR-10, FFHQ, CelebA-HQ, LSUN-Church), and Sections 3.1.2, 3.2, and 3.3 report no latency or time-complexity measurements for WSI or 3D MRI/CT. The claimed contribution is therefore unsupported by the manuscript's own evidence.
[Table 3 and Section 4] The model-selection conclusions in Section 4 ('LDM balances quality and efficiency', 'WDM is a strong option if faster inference is required') rest on Table 3, where FID and inference-time values are collected from separate papers on different datasets with no error bars, no hardware specification, no sampling-step count, and no measurement protocol. For example, WDM inference times are 0.08 s on CIFAR-10 and 1.54 s on LSUN-Church, while LDM times are 0.43 s on FFHQ and 6.80 s on LSUN-Church; these values are not comparable across models because dataset, resolution, and hardware differ. This table cannot support the comparative recommendations advanced in Section 4.
[Section 2.1.1, Eq. (5)] Equation (5) is mathematically garbled: it mixes '-log p_theta(x0|x1)', an undefined term 'KL(p_theta(x_S|x0) || pi(x_S) + (x,z))', and 'log p_theta(x)', so the variational lower bound is not correctly stated. Equations (1) and (4) further use inconsistent notation, with K used for a normal distribution, beta_t as variance in (1) but beta^2 I in (4), and the overline-alpha notation defined inconsistently with the sum/product indices. Because Section 2 is the foundation for the efficiency discussion that follows, this technical exposition needs a thorough rewrite.
[Section 2.2.6 vs Table 2] Section 2.2.6 states 'Generally, LDMs are considered more efficient for large image generation', whereas Table 2 assigns 'Generating large image' as High for WDM, Moderate for LDM, and Low for DDPM. This internal contradiction is not reconciled and is directly relevant to the model-selection guidance in Section 4, further undermining the comparative analysis.
[Section 6, citation [36]] The Conclusion states 'the foundational aspect of DDPMs [36]', but reference [36] is Lugmayr et al.'s RePaint inpainting paper, not Ho et al.'s DDPM paper, which is reference [15]. In a survey whose currency is the accurate mapping of literature, this is a load-bearing citation error that reduces the reader's confidence in the reference list as a whole.

minor comments (6)

[Section 3.1.1.3] 'RePain [36] addressed form inpainting [35, 80]' contains two typos: the method name is RePaint, and 'form' should be 'from'.
[Table 3] 'LSUN-Chruch' is misspelled; it should be 'LSUN-Church'.
[Section 5.1 heading] The heading 'Challanges' is a typo; it should be 'Challenges'.
[Figure 5] The roman-numeral reference mapping is ambiguous: '(iii)' is used both for Wavelet Diffusion and in the Datasets line, and '(xxiv)' is used for both Medical Diffusion and Wavelet SGM, each with different references; please assign unique numerals per reference.
[Eqs. (6)-(8)] The expectation notation E_{epsilon(x)} is nonstandard; the expectation should be over the data x, noise epsilon, and timestep t. Equation (8) also inconsistently writes tau_theta(x) in the condition and tau_theta(x') in the explanation.
[Table 1 note] The note under Table 1 says 'The indicates that all aspects are covered, the shows...', but the symbols are not rendered; the legend is unreadable and should be fixed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this is a survey that restates published results; no load-bearing derivation reduces to its own inputs.

full rationale

This paper is a survey article, not a derivation. It categorizes diffusion models into DDPM, LDM, and WDM, summarizes their mathematical formulations as presented in the original papers, and reports published performance numbers in Table 3. No fitted parameter is subsequently renamed as a prediction, no equation is defined in terms of its own output, and no load-bearing conclusion depends on a self-citation: the author list does not overlap with any cited work in the reference list. The central gap claim in Section 1, that no existing study discusses latency for WSI or 3D MRI/CT, is an accuracy/scope assertion that may be challenged by the paper's own cited survey [11], but this is not circularity; it is a factual adequacy concern about the survey's novelty claim. Likewise, the comparison in Table 3 pools FID and inference-time numbers from different papers and datasets without error bars or hardware details, which threatens the validity of the model-selection conclusions, but this is a benchmarking methodology concern rather than a circular reduction. The equations reproduced for DDPM, LDM, and diffusion-GAN are standard formulations taken from the cited primary sources and are not used to derive results that presuppose those same equations. The claims about WDM speedup are qualitative summaries of the cited wavelet diffusion work, not independent predictions built from fitted inputs. Accordingly, no circular step satisfying the evidentiary standard can be identified, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

This survey introduces no free parameters or new entities. Its central conclusions rest entirely on the reliability of the equations and performance numbers taken from the cited literature.

assumptions (2)

domain assumption The equations reproduced for DDPM, LDM, and WDM are correct as stated in the cited sources.
The review does not rederive these formulations; it imports them from [15], [14], [58] and presents them as background.
domain assumption The performance metrics in Table 3 (FID, Recall, inference time) are accurately transferred from the cited papers and are comparable across models.
The survey does not run experiments; its model-selection conclusions rely on these published numbers. Cross-dataset comparison is especially fragile.

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Cite this review

Pith. "Pith review of Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review." pith.science (2026). https://pith.science/paper/DHQA5IL2

@misc{pith2026250507866,
  author       = {Pith},
  title        = {Pith review of: Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DHQA5IL2}},
  note         = {Machine review of arXiv:2505.07866}
}

read the original abstract

The diffusion model has recently emerged as a potent approach in computer vision, demonstrating remarkable performances in the field of generative artificial intelligence. Capable of producing high-quality synthetic images, diffusion models have been successfully applied across a range of applications. However, a significant challenge remains with the high computational cost associated with training and generating these models. This study focuses on the efficiency and inference time of diffusion-based generative models, highlighting their applications in both natural and medical imaging. We present the most recent advances in diffusion models by categorizing them into three key models: the Denoising Diffusion Probabilistic Model (DDPM), the Latent Diffusion Model (LDM), and the Wavelet Diffusion Model (WDM). These models play a crucial role in medical imaging, where producing fast, reliable, and high-quality medical images is essential for accurate analysis of abnormalities and disease diagnosis. We first investigate the general framework of DDPM, LDM, and WDM and discuss the computational complexity gap filled by these models in natural and medical imaging. We then discuss the current limitations of these models as well as the opportunities and future research directions in medical imaging.

Figures

Figures reproduced from arXiv: 2505.07866 by the authors.

**Figure 2.** An Illustration of the DDPM forward process [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** LDM basic framework encodes data from the pixel space into a latent space. Within this latent space, [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: The wavelet transform method decomposes an image into its frequency components at different scales [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

**Figure 5.** Figure 5: We categorize diffusion models into models, applications, and future directions. Notably, DDPM, LDM, [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

**Figure 6.** Figure 6: Illustrate histopathology images generated using WDM: (a) shows a real image, and (b) presents a [PITH_FULL_IMAGE:figures/full_fig_p022_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Reviewed August 15, 2026 · model on record in the stance chip above.

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