fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting

Alicia Durrer; Bjoern Menze; Florentin Bieder; Florian Kofler; Paul Friedrich; Philippe C. Cattin

REVIEW 3 major objections 4 minor 1 cited by

fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read A variance-preserving noise schedule lets a 3D wavelet diffusion model inpaint healthy brain tissue in two steps at 1.81 s per volume.

desk verdict A practical 3D inpainting speedup: two-step wavelet diffusion in 1.81s per volume, with quality close to prior models, though the claimed superiority over prior DDPMs rests on uncontrolled comparisons. read the letter →

arxiv 2507.13146 v1 pith:KLO6D5JN submitted 2025-07-17 eess.IV cs.CV

Alicia Durrer , Florentin Bieder , Paul Friedrich , Bjoern Menze , Philippe C. Cattin , Florian Kofler This is my paper

classification eess.IVcs.CV

keywords healthytissueinpainting3Ddiffusionmodelwavelettransformvariance-preservingschedulefastsamplingbrainMRIBraTSreconstructionloss

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 slow diffusion-based inpainting of healthy brain tissue does not require adversarial training or hundreds of denoising steps. By transplanting a variance-preserving noise schedule and two reconstruction losses into a 3D wavelet diffusion model, the authors obtain fastWDM3D, which reaches SSIM 0.8571, MSE 0.0079, and PSNR 22.26 on the BraTS inpainting test set using just two time steps and 1.81 s per volume. If correct, this makes diffusion-quality inpainting practical at clinical speed and with modest memory requirements.

What carries the argument

The key components are (i) the variance-preserving (VP) noise schedule of Song et al. [27], whose cumulative perturbation is independent of the number of time steps T, so full perturbation is reached even for small T; (ii) a reconstruction loss composed of an image-space loss between prediction and ground truth and an additional region-specific loss on the masked area; and (iii) a 3D discrete wavelet transform, which halves spatial resolution into eight coefficient subbands and lets the model generate compact wavelet representations. Working together, they let a denoiser trained for T=1000-style objectives produce good inpaintings at T=2 without adversarial components.

What would settle it

Run fastWDM3D and the original WDM3D with matched training iterations, batch size, and hardware, swapping only the noise schedule and loss. If the T=2 gains vanish or the 800x speed advantage disappears under identical measurement conditions, the central claim is refuted.

Watch

Extended reading notes

Core claim

The central claim is that the variance-preserving (VP) noise schedule and the chosen reconstruction losses, previously used in a GAN-based 2D generator, transfer to a GAN-free 3D wavelet diffusion model and are what enable competitive inpainting at T=2 time steps. The best configuration, fastWDM3D with T=2 trained for 120k iterations, outperforms all DDPM configurations previously assessed on the same BraTS test set, including the original WDM3D with T=1000 and a linear schedule, while sampling in 1.81 s per volume, up to roughly 800x faster. The authors argue that because the adversarial loss never decreased during training of the GAN version, dropping the discriminator costs nothing and simplifies the model.

Load-bearing premise

The superiority and speed claims assume that the test-set scores and sampling times reported in earlier papers were measured under comparable preprocessing, normalization, metric implementation, and hardware; the ablations that would isolate the variance-preserving schedule and the reconstruction losses from extra training iterations and batch size were not run.

Editorial extensions

If this is right

FastWDM3D can generate pseudo-healthy baselines for tumor growth modeling and image registration in seconds rather than minutes, making such pipelines clinically practical.
Adversarial training is not needed for high-quality 3D inpainting in this setting, so simpler GAN-free architectures can be used with less memory and shorter training.
The VP schedule remains effective across T=2, 4, and 8, suggesting the method tolerates varying step counts without retuning.
Compared with previously assessed DDPMs for healthy tissue inpainting, fastWDM3D achieves better SSIM, MSE, and PSNR while being up to roughly 800x faster during sampling.

Reading between the lines

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

If the gains truly come from the VP schedule and reconstruction losses, the same recipe may transfer to other 3D medical inpainting tasks and possibly to unconditional volume generation.
The schedule's step-count independence hints that the same trained model could offer a speed-quality tradeoff at inference time by simply changing T, without retraining.
A controlled ablation with matched training iterations, batch size, and hardware would settle whether the improvement is due to the schedule, the loss, or simply more training.
The qualitative advantage over the challenge winner's blurry inpainting suggests that perceptual quality metrics beyond SSIM/PSNR might better capture the practical benefit.

Share X Bluesky LinkedIn Reddit HN

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 should know two things about this paper. First, it demonstrates that a 3D wavelet diffusion model (WDM3D) can inpaint healthy tissue on BraTS in two sampling steps, producing a full volume in 1.81 seconds with SSIM around 0.86. That is a real, practically useful speedup over 1000-step DDPMs. Second, the paper's claim that it achieves 'superior performance metrics' over all other assessed DDPMs is not established, because that comparison uses published numbers from a prior paper without controlling for preprocessing, normalization, metric implementation, or hardware.

What is genuinely new: the authors adapt WDDGAN to 3D inpainting, find that the adversarial loss does nothing useful, remove the discriminator, and then show that the variance-preserving schedule plus reconstruction losses can be transplanted into WDM3D. The observation that a GAN component is unnecessary for this task is clean and useful. They also compare different time steps in a coherent internal experiment, and they release code.

Soft spots, in proportion: the headline quality comparison (Table 4) mixes their measurement protocol with numbers from Durrer et al. The stress-test note is right that if the baselines were evaluated under a different protocol, the quality advantage could shrink or disappear. The speedup is solid because it is arithmetic (2 vs 1000 steps), but the 'superior metrics' should be read as "better under our evaluation setup." Second, the best configuration (T=2, 120k iterations) is selected on the test set and no multiple-seed variance is reported, so differences between T=2 and T=4 at 120k are within noise. Third, attribution of the gains to the VP schedule and loss is confounded with more training iterations and a different batch size; the authors themselves acknowledge that an ablation would be beneficial.

Who is this for? Researchers working on fast inpainting for pseudo-healthy baseline generation, or anyone who wants a quick 3D diffusion baseline. The method itself seems sound and the internal experiments are honestly reported. The flaws are mostly in the strength of the comparative claims, not in the core result. This deserves a serious referee, and with a controlled comparison or more cautious wording, it could be a useful contribution.

Recommendation: engage with it, but push for a controlled re-evaluation against prior methods under the same protocol, and at least one repeated-seed experiment. The core speed-accuracy tradeoff is real.

Referee Report

3 major / 4 minor

Summary. The paper proposes fastWDM3D, a 3D healthy-tissue inpainting method derived from WDM3D by substituting a variance-preserving (VP) noise schedule and reconstruction losses for the original linear schedule and wavelet-coefficient MSE loss, and by using very few diffusion steps (T=2). The authors first show that removing the GAN component from a 3D WDDGAN (GO3D) does not hurt quality, then apply the VP schedule and reconstruction losses to WDM3D. On the BraTS 2023 inpainting test set, fastWDM3D with T=2 and 120k training iterations reports SSIM 0.8571, MSE 0.0079, PSNR 22.26, and an average sampling time of 1.81 s per volume. The paper claims this is superior to previously assessed DDPMs for this task while being up to about 800x faster.

Significance. If the claims hold, fastWDM3D is a practically attractive result: it combines the sample quality of diffusion models with two-step sampling, removes the need for adversarial training, halves training memory relative to GO3D, and ships with public code and a public benchmark. The internal comparison between WDDGAN3D and GO3D is a useful ablation showing that the adversarial loss is not essential for this inpainting task. However, the central quality claim against prior DDPMs rests on an uncontrolled cross-paper comparison, and the best configuration is selected using test-set metrics. These issues must be addressed before the superiority claim can be considered established.

major comments (3)

[Section 4, Table 4] The claim that fastWDM3D achieves 'better performance metrics than all other assessed DDPMs' rests on juxtaposing metrics and AST values reported in [6] with those measured by the authors, without controlling preprocessing (0.5-percentile clipping, scaling to [-1,1], cropping to 128^3), normalization of predictions, metric implementation, or hardware. The AST values are explicitly noted to depend on server utilization, making the 800x speed comparison non-reproducible from the reported data. Since these uncontrolled factors can shift SSIM/MSE/PSNR by amounts comparable to or larger than the observed gaps (e.g., WDM3D SSIM 0.61 vs fastWDM3D 0.86), the superiority claim is not yet established. Please re-evaluate the baselines with the exact evaluation protocol used for fastWDM3D, or restrict the claim to speed and to comparisons made under identical conditions.
[Section 3, Tables 1 and 2] The configuration labeled best (fastWDM3D, T=2, 120k iterations) is selected using test-set metrics: the text says 'Based on Tables 1 and 2 ... we labeled this as our best configuration'. This makes the reported headline scores selection outcomes rather than unbiased test-set evaluations. Model selection and hyperparameter choices should be made on the validation set, with the test set used only once, or the paper should explicitly acknowledge the selection bias. In addition, only a single training run per configuration is reported, so it is unclear whether the small metric differences between configurations (e.g., SSIM 0.8553 vs 0.8571) are significant.
[Section 4 and Section 2.5] The paper attributes the improvement to the VP schedule and the reconstruction losses ('Changing the schedule and the loss allowed a vast improvement'), but the comparison to WDM3D in [6] changes several factors simultaneously: the noise schedule, the loss function, the architecture (wavelet coefficients handling), the number of time steps (1000 vs 2), training iterations, batch size, and evaluation code. The manuscript itself concedes that 'an ablation study disentangling the influence of the variance schedule, the loss, and the model architecture would be beneficial'. To support the causal claim, a proper ablation on fastWDM3D varying the schedule and loss while holding all other factors fixed is needed; without it, the 'key role' statement is not supported by the presented evidence.

minor comments (4)

[Table 2] The GO3D T=64, 40k-iteration row reports MSE as 0.0083±0.0643; the standard deviation is an order of magnitude larger than the mean and inconsistent with neighboring rows. Please verify this value.
[Equation (3)] The notation uses α_t for both the per-step value 1-β_t and the cumulative product over s=1..t. Using \bar{α}_t for the cumulative product would avoid ambiguity, especially because Fig. 1 plots the cumulative quantity.
[Figure 1] The legend entry 'T = 1000 (VP & L)' is confusing because the LA schedule is also discussed and plotted for T=1000. Please clarify which curves correspond to which schedules at T=1000.
[Section 3] The code repository is referenced by URL only; please provide a version or commit hash so that the exact experimental setup is reproducible.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: fastWDM3D's headline scores are empirical test-set results on the external BraTS benchmark, and the self-cited baseline comparison and conceded missing ablation are validity caveats rather than reductions by construction.

full rationale

The paper's derivation chain is empirical and self-contained against an external benchmark: the headline numbers (SSIM 0.8571, MSE 0.0079, PSNR 22.26, 1.81 s per volume, Tables 2 and 3) are fastWDM3D's own scores on the non-public BraTS 2023 inpainting test set, computed with the authors' evaluation code on ground-truth data, so they are neither fitted parameters nor quantities forced by the method's equations. The variance-preserving schedule (Eq. 3, cited from Song et al. [27]) and its perturbation curves (Fig. 1) are mathematical facts imported from prior work, not derived results that smuggle in the conclusion; the choice of VP over the LA schedule is supported by an internal, controlled comparison (Table 1: same batch size, blocks, learning rate, and 100 training epochs). The causal claim that 'Changing the schedule and the loss allowed a vast improvement' (Section 4) is, however, drawn from Table 4, which juxtaposes fastWDM3D against the authors' own prior WDM3D [6] while changing T (1000 to 2), the schedule, the loss, training iterations, and batch size all at once, and the paper itself concedes that 'an ablation study disentangling the influence of the variance schedule, the loss, and the model architecture would be beneficial' (Section 4); this is a confound that limits the attribution, but it is not a circular reduction, since the [6] scores are published empirical values from a peer-reviewed prior study rather than outputs of the present paper's assumptions. Similarly, the timing comparison rests on AST values that the authors flag as server-dependent ('can vary as it depends on the overall server utilization', Tables 2 and 4), which is an uncontrolled-hardware caveat of the same kind, and the selection of the best configuration from several test-set evaluations (Tables 1-2) risks optimistic peeking but is not a tautology. No equation in the paper is defined in terms of the target result, and no test-set-derived value is renamed as a prediction, so no specific circular step can be exhibited.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim depends on a small number of hand-chosen or inherited components: the VP schedule parameters (from Song et al. [27]), the data-selected number of diffusion steps T=2, the reconstruction loss formulation, and the comparability of non-public test-set scores across papers. No new theoretical entities are introduced. The training pipeline uses standard DWT and Palette-style conditioning.

free parameters (3)

VP schedule beta_min = 0.1
Adopted from Song et al. [27]; sets the minimum noise level in Eq. 3. The two-step performance depends on this choice, though it is not tuned on the BraTS data.
VP schedule beta_max = 20
Adopted from Song et al. [27]; sets maximum noise. Together with beta_min it controls the cumulative signal preservation alpha_t for small T.
Number of diffusion steps T = 2
Selected as the best configuration on the BraTS test set (Table 2). The headline scores are reported for this post-hoc choice, so T functions as a data-selected parameter in the central claim.

assumptions (5)

domain assumption The variance-preserving schedule (Eq. 3) with beta_min=0.1 and beta_max=20 provides sufficient forward perturbation for small T such that the reverse process can recover the inpainted image in two steps.
This is the core empirical premise behind using T=2; it is validated only by the reported test-set numbers, not by a formal argument.
domain assumption The BraTS inpainting test set metrics reported in prior work ([6], [30]) are comparable to those computed in this paper despite possible differences in preprocessing and normalization.
The claims of superior performance in Tables 3 and 4 rely on comparing numbers from different papers without rerunning prior methods.
domain assumption Palette-style conditioning, concatenating voided image, mask, and noisy ground truth as a 24-channel input, is sufficient for the model to learn the inpainting task.
Used in Section 2.5; no theoretical guarantee, but standard in image-to-image diffusion.
domain assumption The selected reconstruction losses L_yhat + L_yhat_m measured in image space provide a suitable training signal for high-quality inpainting.
The choice of losses is central to the method and is justified empirically, not derived.
standard math The discrete wavelet transform used for 3D volumes is invertible and the concatenated 8-subband representation preserves the information needed for IDWT reconstruction.
Invoked in Section 2.2; standard result.

how reviews work

0 comments

Cite this review

Pith. "Pith review of fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting." pith.science (2026). https://pith.science/paper/KLO6D5JN

@misc{pith2026250713146,
  author       = {Pith},
  title        = {Pith review of: fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KLO6D5JN}},
  note         = {Machine review of arXiv:2507.13146}
}

read the original abstract

Healthy tissue inpainting has significant applications, including the generation of pseudo-healthy baselines for tumor growth models and the facilitation of image registration. In previous editions of the BraTS Local Synthesis of Healthy Brain Tissue via Inpainting Challenge, denoising diffusion probabilistic models (DDPMs) demonstrated qualitatively convincing results but suffered from low sampling speed. To mitigate this limitation, we adapted a 2D image generation approach, combining DDPMs with generative adversarial networks (GANs) and employing a variance-preserving noise schedule, for the task of 3D inpainting. Our experiments showed that the variance-preserving noise schedule and the selected reconstruction losses can be effectively utilized for high-quality 3D inpainting in a few time steps without requiring adversarial training. We applied our findings to a different architecture, a 3D wavelet diffusion model (WDM3D) that does not include a GAN component. The resulting model, denoted as fastWDM3D, obtained a SSIM of 0.8571, a MSE of 0.0079, and a PSNR of 22.26 on the BraTS inpainting test set. Remarkably, it achieved these scores using only two time steps, completing the 3D inpainting process in 1.81 s per image. When compared to other DDPMs used for healthy brain tissue inpainting, our model is up to 800 x faster while still achieving superior performance metrics. Our proposed method, fastWDM3D, represents a promising approach for fast and accurate healthy tissue inpainting. Our code is available at https://github.com/AliciaDurrer/fastWDM3D.

Figures

Figures reproduced from arXiv: 2507.13146 by the authors.

**Figure 1.** Comparison of αt for the L-, LA- and VP schedule after normalizing all individual T to [0,1]. The L- and VP schedules have the same curve for T = 1000. The L schedule only provides full perturbation for large T while the LA schedule perturbs the image too early if T is large. The VP schedule is applicable for low and large T. 2.1 Denoising Diffusion Probabilistic Models In DDPMs [13], noise gradually perturbs an in… view at source ↗

**Figure 2.** Axial (top) and coronal (bottom) view of an image of the validation set. Comparison of the BraTS 2023 inpainting challenge podium and our best model, fastWDM3D (T = 2), given the voided input image (voided) and the ground truth (GT) [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping
physics.med-ph 2026-07 conditional novelty 6.0 of 10

A self-supervised, physics-regularized neural reconstruction produces high-resolution fetal brain T2 maps at 0.55 T and 1.5 T from multi-echo MRI, with reduced acquisition time.

Reference graph

Works this paper leans on

31 extracted references · 20 canonical work pages · cited by 1 Pith paper

[6]

In: MICCAI Workshop on Deep Generative Models

Durrer, A., et al.: Denoising diffusion models for 3d healthy brain tissue inpainting. In: MICCAI Workshop on Deep Generative Models. pp. 87–97. Springer (2024)

work page 2024
[1]

https://github.com/BrainLesion/ BraTS, last accessed 2025/06/24

BraTS challenge: Top performing algorithms. https://github.com/BrainLesion/ BraTS, last accessed 2025/06/24

work page 2025
[2]

arXiv preprint arXiv:2107.02314 (2021)

Baid, U., et al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor seg- mentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)

arXiv 2021
[3]

Scientific data4(1), 1–13 (2017)

Bakas, S., et al.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data4(1), 1–13 (2017)

work page 2017
[4]

Dadar, M., Potvin, O., Camicioli, R., Duchesne, S., for the Alzheimer’s Disease Neuroimaging Initiative: Beware of white matter hyperintensities causing system- atic errors in freesurfer gray matter segmentations! Human Brain Mapping42(9), 2734–2745 (2021)

work page 2021
[5]

In: International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, pp

Durrer, A., Cattin, P.C., Wolleb, J.: Denoising diffusion models for inpainting of healthy brain tissue. In: International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, pp. 35–45. Springer (2023)

work page 2023
[7]

Medical Image Analysis83, 102672 (2023)

Ezhov, I., et al.: Learn-morph-infer: a new way of solving the inverse problem for brain tumor modeling. Medical Image Analysis83, 102672 (2023)

work page 2023
[8]

arXiv preprint arXiv:2410.12557 (2024)

Frans,K.,Hafner,D.,Levine,S.,Abbeel,P.:Onestepdiffusionviashortcutmodels. arXiv preprint arXiv:2410.12557 (2024)

arXiv 2024

Show all 31 references

[9]

In: MICCAI Workshop on Deep Generative Models

Friedrich, P., Wolleb, J., Bieder, F., Durrer, A., Cattin, P.C.: Wdm: 3d wavelet dif- fusion models for high-resolution medical image synthesis. In: MICCAI Workshop on Deep Generative Models. pp. 11–21. Springer (2024) 10 A. Durrer et al

2024
[10]

ACM Transactions on Graphics (TOG)40(4), 1–11 (2021)

Gal, R., Hochberg, D.C., Bermano, A., Cohen-Or, D.: Swagan: A style-based wavelet-driven generative model. ACM Transactions on Graphics (TOG)40(4), 1–11 (2021)

2021
[11]

Advances in neural information processing systems27(2014)

Goodfellow, I.J., et al.: Generative adversarial nets. Advances in neural information processing systems27(2014)

2014
[12]

Advances in neural information processing systems35, 478–491 (2022)

Guth, F., Coste, S., De Bortoli, V., Mallat, S.: Wavelet score-based generative modeling. Advances in neural information processing systems35, 478–491 (2022)

2022
[13]

Advances in neural information processing systems33, 6840–6851 (2020)

Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems33, 6840–6851 (2020)

2020
[14]

In: International ChallengeonCross-ModalityDomainAdaptationforMedicalImageSegmentation, pp

Huo, J., Liu, Y., Granados, A., Ourselin, S., Sparks, R.: Unleash the power of 2d pre-trained model for 3d t1-weighted brain mri inpainting. In: International ChallengeonCross-ModalityDomainAdaptationforMedicalImageSegmentation, pp. 3–10. Springer (2023)

2023
[15]

Nature Machine Intelligence5(7), 799–810 (2023)

Karargyris, A., Umeton, R., Sheller, M.J., et al.: Federated benchmarking of med- ical artificial intelligence with medperf. Nature Machine Intelligence5(7), 799–810 (2023)

2023
[16]

Advances in Neural Information Processing Systems35, 26565–26577 (2022)

Karras,T.,Aittala,M.,Aila,T.,Laine,S.:Elucidatingthedesignspaceofdiffusion- based generative models. Advances in Neural Information Processing Systems35, 26565–26577 (2022)

2022
[17]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4401–4410 (2019)

2019
[18]

Advances in neural information processing systems34, 21696–21707 (2021)

Kingma, D., Salimans, T., Poole, B., Ho, J.: Variational diffusion models. Advances in neural information processing systems34, 21696–21707 (2021)

2021
[19]

arXiv preprint arXiv:2305.08992 (2023)

Kofler, F., et al.: The brain tumor segmentation (brats) challenge 2023: Local synthesis of healthy brain tissue via inpainting. arXiv preprint arXiv:2305.08992 (2023)

2023 arXiv
[20]

Li, L., et al.: Autodiffusion: Training-free optimization of time steps and architec- turesforautomateddiffusionmodelacceleration.In:ProceedingsoftheIEEE/CVF International Conference on Computer Vision. pp. 7105–7114 (2023)

2023
[21]

In: Proceedings of the IEEE/CVF winter conference on applications of computer vision

Lin,S.,Liu,B.,Li,J.,Yang,X.:Commondiffusionnoiseschedulesandsamplesteps are flawed. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 5404–5411 (2024)

2024
[22]

IEEE transactions on medical imaging34(10), 1993–2024 (2014)

Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging34(10), 1993–2024 (2014)

2014
[23]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Phung, H., Dao, Q., Tran, A.: Wavelet diffusion models are fast and scalable image generators. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10199–10208 (2023)

2023
[24]

In: ACM SIGGRAPH 2022 conference proceedings

Saharia, C., et al.: Palette: Image-to-image diffusion models. In: ACM SIGGRAPH 2022 conference proceedings. pp. 1–10 (2022)

2022
[25]

arXiv preprint arXiv:2202.00512 (2022)

Salimans, T., Ho, J.: Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512 (2022)

2022 arXiv
[26]

arXiv preprint arXiv:2104.02600 (2021)

San-Roman, R., Nachmani, E., Wolf, L.: Noise estimation for generative diffusion models. arXiv preprint arXiv:2104.02600 (2021)

2021 arXiv
[27]

arXiv preprint arXiv:2011.13456 (2020)

Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score- based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)

2020 arXiv
[28]

arXiv preprint arXiv:2112.07804 (2021) fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting 11

Xiao, Z., Kreis, K., Vahdat, A.: Tackling the generative learning trilemma with denoising diffusion gans. arXiv preprint arXiv:2112.07804 (2021) fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting 11

2021 arXiv
[29]

arXiv preprint arXiv:2412.01243 (2024)

Ye, Z., Chen, Z., Li, T., Huang, Z., Luo, W., Qi, G.J.: Schedule on the fly: Diffusion time prediction for faster and better image generation. arXiv preprint arXiv:2412.01243 (2024)

2024 arXiv
[30]

In: International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, pp

Zhang, J., Chen, K., Weng, Y.: Synthesis of healthy tissue within tumor area via u- net. In: International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, pp. 233–240. Springer (2023)

2023
[31]

In: European Conference on Computer Vision

Zheng, T., et al.: Beta-tuned timestep diffusion model. In: European Conference on Computer Vision. pp. 114–130. Springer (2024)

2024

Pith tools

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

[1] [6]

In: MICCAI Workshop on Deep Generative Models

Durrer, A., et al.: Denoising diffusion models for 3d healthy brain tissue inpainting. In: MICCAI Workshop on Deep Generative Models. pp. 87–97. Springer (2024)

work page 2024

[2] [1]

https://github.com/BrainLesion/ BraTS, last accessed 2025/06/24

BraTS challenge: Top performing algorithms. https://github.com/BrainLesion/ BraTS, last accessed 2025/06/24

work page 2025

[3] [2]

arXiv preprint arXiv:2107.02314 (2021)

Baid, U., et al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor seg- mentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)

arXiv 2021

[4] [3]

Scientific data4(1), 1–13 (2017)

Bakas, S., et al.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data4(1), 1–13 (2017)

work page 2017

[5] [4]

Dadar, M., Potvin, O., Camicioli, R., Duchesne, S., for the Alzheimer’s Disease Neuroimaging Initiative: Beware of white matter hyperintensities causing system- atic errors in freesurfer gray matter segmentations! Human Brain Mapping42(9), 2734–2745 (2021)

work page 2021

[6] [5]

In: International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, pp

Durrer, A., Cattin, P.C., Wolleb, J.: Denoising diffusion models for inpainting of healthy brain tissue. In: International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, pp. 35–45. Springer (2023)

work page 2023

[7] [7]

Medical Image Analysis83, 102672 (2023)

Ezhov, I., et al.: Learn-morph-infer: a new way of solving the inverse problem for brain tumor modeling. Medical Image Analysis83, 102672 (2023)

work page 2023

[8] [8]

arXiv preprint arXiv:2410.12557 (2024)

Frans,K.,Hafner,D.,Levine,S.,Abbeel,P.:Onestepdiffusionviashortcutmodels. arXiv preprint arXiv:2410.12557 (2024)

arXiv 2024