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REVIEW 3 major objections 6 minor 1 cited by

SimGen: A Diffusion-Based Framework for Simultaneous Surgical Image and Segmentation Mask Generation

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A single diffusion denoiser can generate a surgical image and its segmentation mask together from noise.

desk verdict SimGen is a plausible diffusion extension worth reviewing, but the 'six datasets' claim is not supported by the tables—the strongest baseline comparison covers only three datasets. read the letter →

arxiv 2501.09008 v1 pith:QU3LFXIQ submitted 2025-01-15 cs.CV

Aditya Bhat , Rupak Bose , Chinedu Innocent Nwoye , Nicolas Padoy This is my paper · ORCID
classification cs.CV
keywords diffusionmodelssurgicalimagesynthesispairedimage-maskgenerationsegmentationmaskCanonicalFibonacciLatticeSemanticInceptionDistancesyntheticmedicaldataDDPM
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

SimGen aims to solve a data bottleneck in surgical AI: real surgical images with expert segmentation masks are expensive, time-consuming, and often restricted by ethics or regulation. The paper introduces a new task, simultaneous image-and-mask generation, and a DDPM-based model that produces a photorealistic surgical image and its boundary-aligned segmentation mask together from noise. The key move is to treat the pair as a single 6-channel signal and let one diffusion denoiser learn the joint distribution, using a Canonical Fibonacci Lattice to encode class labels into well-separated RGB colors. Across six public surgical datasets, SimGen reports lower FID/KID and semantic sFID/sKID scores than DCGAN, Pix2Pix, and convolutional VAE baselines. A UNet trained only on SimGen-generated pairs reaches roughly 42% IoU on real test data, suggesting the synthetic pairs are usable when real data cannot be released.

What carries the argument

The load-bearing mechanism is the concatenated 6-channel diffusion pair $(x_0 \oplus y_0)$, where an RGB image and its class-colored mask are noised and denoised together by one residual U-Net that regresses the noise with the standard DDPM mean-squared-error loss. Because no explicit alignment head or conditioning module is used, all spatial correspondence must be learned implicitly from the shared noise schedule; the authors call this implicit learning 'cross-correlation priors'. Around this core sit two supporting pieces: the Canonical Fibonacci Lattice, which assigns each semantic class to a golden-angle-separated point on the RGB unit sphere so class colors do not overlap and cosine similarity can recover labels, and Semantic Inception Distance (sFID/sKID), which evaluates each mask-defined region separately, thereby penalizing boundary misalignment and class assignment errors that global FID would miss.

What would settle it

Compare the semantic alignment of SimGen-generated masks with masks produced by running a strong pretrained segmentation model on the same SimGen-generated images: if the segmenter's masks achieve lower sFID/sKID than the jointly generated masks on a held-out set, the claim that joint generation preserves image-mask alignment is contradicted.

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Extended reading notes

Core claim

The central claim is that a single DDPM-style denoiser, applied to the concatenated 6-channel tensor of an image and its mask, can learn cross-correlation priors between the continuous image distribution and the discrete mask distribution, and therefore generate both modalities jointly with mutual alignment. The paper introduces the task, the SimGen architecture (a residual U-Net with two residual skip connections and a learnable down/up sampler), and the CFL projection that maps class labels onto a uniformly spaced RGB unit sphere; at inference, class labels are recovered by cosine similarity to the CFL dictionary. The authors report that SimGen outperforms all baselines on FID, KID, semantic FID, and semantic KID on six datasets (CholecSeg8k, CaDISv2, EndoVIS 2017, Kvasir-SEG, Endoscapes, CholecInstanceSeg), and that the CFL ablation improves mean class-based FID from 183.9 to 133.4 on CholecSeg8k. Downstream, a UNet trained solely on generated pairs achieves about 42% IoU against 52.1% for real-data training, which the paper interprets as evidence that synthetic pairs remain useful when regulations limit human data release.

Load-bearing premise

The load-bearing premise is that one diffusion denoiser, trained only with a plain mean-squared-error noise-prediction loss on the concatenated 6-channel image-mask tensor, will learn the spatial correspondence between image and mask well enough to keep them aligned during generation, with no explicit alignment loss or conditioning mechanism.

Editorial extensions

If this is right

  • If SimGen is right, institutions without permission to share real surgical video can still generate paired image-mask data for training segmentation models; the reported ~42% IoU is a baseline for that regime.
  • When real data is scarce but a larger institution can release only model outputs, augmenting small real subsets with SimGen-generated data from a broader superset improves downstream IoU by up to 20% in the reported experiments.
  • Generated masks align better with generated images than masks predicted from the images by a separately trained segmentation model (SegFormer), which the paper presents as evidence for paired generation over post-hoc segmentation in simulation.
  • Training a diffusion model on real data from one distribution and using it where class distributions differ shifts downstream performance, making SimGen outputs a concrete testbed for domain adaptation and pretraining.

Reading between the lines

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

  • The concatenation recipe is not specific to surgery; the same 6-channel joint-denoiser idea could in principle generate paired RGB-D, image-normal, or image-text-layout data, and the CFL encoding offers a ready label discretization for any categorical channel.
  • Because alignment is never explicitly enforced, the rarity of failures like a 'floating' grasper suggests the implicit prior is strong but not guaranteed; a cheap test would be to measure sFID after training on pairs with artificially shifted masks to see whether the model learns shift invariance rather than true correspondence.
  • The 42%-versus-52.1% IoU gap quantifies the domain shift between synthetic and real surgical data, which the paper leaves as future work; one could exploit that gap directly by using the generative model as a source of unlimited unlabeled images for self-supervised pretraining.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. SimGen is a DDPM-based generative model that jointly produces a surgical image and its segmentation mask by denoising a concatenated 6-channel image-mask tensor. The paper introduces a Canonical Fibonacci Lattice (CFL) to encode discrete class labels into RGB space, and a new evaluation metric, Semantic Inception Distance (sFID/sKID), for measuring image-mask alignment and class correctness. The method is evaluated on six public surgical datasets with FID/KID and sFID/sKID, and its downstream utility is tested by training a UNet on generated image-mask pairs. The central claim is that SimGen outperforms the baselines (Pix2Pix, DCGAN, VAE) across these datasets on image and semantic inception distance metrics.

Significance. If the empirical claims are fully supported, the paper would make a useful contribution to surgical data synthesis: it introduces a task (paired image-mask generation), a simple diffusion-based solution, a new metric (SID), and a downstream analysis showing that synthetic pairs are usable when real data cannot be shared. The ablation of the CFL encoding and the explicit failure-case discussion are also strengths. However, the current evidence does not substantiate the headline 'six datasets' claim because baseline results are missing for three of the six datasets, and the SID metric is not defined precisely enough to be reproduced. The downstream utility experiment (about 42% IoU) is a valuable sanity check but does not substitute for the missing baseline comparisons.

major comments (3)
  1. [§5.1, Tables 2 and 3] The abstract claims that SimGen 'outperforms baselines across six public datasets' on image and semantic inception distance metrics, but Tables 2 and 3 report baseline (Pix2Pix, DCGAN, VAE) results only for CaDISv2, CholecSeg8k, and Kvasir-SEG; all baseline cells for CholecInstanceSeg, Endoscapes, and EndoVIS are dashes. Section 5.1 justifies this by stating that these datasets 'belongs to the same source as CholecSeg8k,' but same-source status does not transfer performance: the datasets differ substantially in size (8080 vs. 343 vs. 1800 training images) and class composition, and SimGen's own FID varies from 57.5 to 217.9 across them. Without same-protocol baseline runs on these three datasets, the headline comparison is unsupported; the empirically supported comparison covers only three datasets.
  2. [§3.5] The Semantic Inception Distance (sFID/sKID) is the basis for the main quantitative comparison in Tables 3 and 4, but Section 3.5 defines it only verbally. The paper does not specify how the Inception feature map is cropped or masked per semantic region, how the per-class distances are computed and averaged, or which reference distribution is used for the real data. Without an explicit formula or algorithm, the reported sFID/sKID values cannot be reproduced or independently verified, and the 'mean per-class' aggregation in Table 3 is undefined.
  3. [§3.3–3.4] The claimed 'cross-correlation priors' are not formalized: the model simply concatenates image and mask channels (x0 ⊕ y0) and trains with the standard DDPM MSE loss on the joint noise prediction. The paper does not demonstrate that this concatenation is sufficient to align the mask with the image, nor does it compare against alternative mechanisms such as generating the image unconditionally and obtaining the mask with a separate segmentation network, or generating the mask first and conditioning image generation on it. Because paired alignment is the central promise of the method, this missing comparison leaves the main methodological claim unjustified. The SegFormer comparison in Fig. 10 is qualitative only, on three datasets, and does not quantify alignment.
minor comments (6)
  1. [§1, §6] Contribution 5 and the Conclusion state 'five surgical datasets,' while the Abstract and Section 5.1 claim 'six public datasets'; please reconcile this inconsistency.
  2. [§3.3] The reference to the Central Limit Theorem is inaccurate: the forward process adds Gaussian noise explicitly, making the terminal marginal Gaussian by construction; CLT is not needed and is misleading.
  3. [Table 1] EndoVIS is listed twice (instruments and parts) but Tables 2 and 3 report a single EndoVIS column; clarify which task or mask type was used for the reported numbers.
  4. [§5.5, Figure 14] The IoU values differ across the text (§1 reports 52.1% vs. 41.7%, §5.5 says 'approximately 42%', and Figure 14's 'Gen' bar appears to show about 42%); specify the exact numbers and the downstream training protocol (dataset, UNet variant, number of iterations).
  5. [References] CaDISv2 is cited as [18] in Section 4.1 and as [32] in Table 1; please harmonize the citations for the same dataset.
  6. [Reproducibility] The paper does not mention a code release or a reproducibility statement; adding a link or detailed hyperparameters for the baselines would help.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the method is a standard diffusion training pipeline evaluated on held-out external data; missing baseline coverage is a completeness issue, not circularity.

full rationale

SimGen is trained on public surgical datasets with the standard DDPM MSE noise-prediction loss, and the joint image-mask generation is implemented as concatenated 6-channel input to a ResUNet. All quantitative claims are evaluated against held-out real test splits using FID/KID and the author-introduced sFID/sKID, and the SID family is applied symmetrically to SimGen and the baselines under the same protocol. The Canonical Fibonacci Lattice is a fixed, closed-form color projection; it is not fitted from the data, and the downstream UNet IoU of about 42% on real test data is an external benchmark that does not reduce to the training objective. The paper cites several self-authored dataset papers (Endoscapes, CaDIS challenge) and prior text-to-image work by the same group, but these citations provide datasets and context, not the load-bearing derivation, so they do not constitute circularity. The main weakness is evidential rather than circular: Tables 2 and 3 report no baseline entries for Endoscapes, CholecInstanceSeg, and EndoVIS, and Section 5.1 explains that baselines were not retrained for these datasets because they 'belongs to the same source as CholecSeg8k'. This leaves the abstract's 'outperforming baselines across six public datasets' claim supported only for three datasets, and it is a legitimate reviewer concern about completeness and comparison validity, not a case where a prediction reduces by construction to a fitted input or a self-citation. No step in the paper's derivation chain is equivalent to its own inputs, so the circularity score is 0.

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

The central claim relies on standard diffusion assumptions plus the unvalidated design choice that a single denoiser over concatenated channels learns the joint distribution. The main free parameters are architecture and training hyperparameters chosen without sensitivity analysis.

free parameters (3)
  • Base feature map width = 64
    Chosen by hand; the ablation in Fig. 12 shows quality varies with feature size, so the central results depend on this choice.
  • Number of diffusion timesteps = 250
    Chosen by hand; not ablated; the claim relies on this schedule reaching a good prior.
  • Learning rate and batch size = 1e-4 / 16
    Standard choices; no sensitivity reported.
assumptions (3)
  • standard math DDPM forward and reverse process as defined by Ho et al. (2020)
    The method is built entirely on this framework.
  • domain assumption Terminal noise after 250 timesteps is approximately Gaussian (justified informally by CLT)
    Section 3.3 invokes CLT to claim stable noise; this is a standard property but the schedule length is a practical choice.
  • ad hoc to paper Concatenating image and mask channels and minimizing shared MSE denoising loss is sufficient to learn image-mask dependencies
    Called 'cross-correlation priors' in Section 3.3; no derivation or comparison with separate streams or cross-attention.

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Cite this review

Pith. "Pith review of SimGen: A Diffusion-Based Framework for Simultaneous Surgical Image and Segmentation Mask Generation." pith.science (2026). https://pith.science/paper/QU3LFXIQ

@misc{pith2026250109008,
  author       = {Pith},
  title        = {Pith review of: SimGen: A Diffusion-Based Framework for Simultaneous Surgical Image and Segmentation Mask Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QU3LFXIQ}},
  note         = {Machine review of arXiv:2501.09008}
}
read the original abstract

Acquiring and annotating surgical data is often resource-intensive, ethical constraining, and requiring significant expert involvement. While generative AI models like text-to-image can alleviate data scarcity, incorporating spatial annotations, such as segmentation masks, is crucial for precision-driven surgical applications, simulation, and education. This study introduces both a novel task and method, SimGen, for Simultaneous Image and Mask Generation. SimGen is a diffusion model based on the DDPM framework and Residual U-Net, designed to jointly generate high-fidelity surgical images and their corresponding segmentation masks. The model leverages cross-correlation priors to capture dependencies between continuous image and discrete mask distributions. Additionally, a Canonical Fibonacci Lattice (CFL) is employed to enhance class separability and uniformity in the RGB space of the masks. SimGen delivers high-fidelity images and accurate segmentation masks, outperforming baselines across six public datasets assessed on image and semantic inception distance metrics. Ablation study shows that the CFL improves mask quality and spatial separation. Downstream experiments suggest generated image-mask pairs are usable if regulations limit human data release for research. This work offers a cost-effective solution for generating paired surgical images and complex labels, advancing surgical AI development by reducing the need for expensive manual annotations.

Figures

Figures reproduced from arXiv: 2501.09008 by the authors.

Figure 1
Figure 1. Sample outputs of SimGen across the 6 explored datasets. For each pair, the generated photorealistic image is on left [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustration of the proposed Fibonacci projection of [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Architecture of the proposed SimGen model showing its several key components that work together to generate paired [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: A random set of 6 generated image-mask pairs from the CholecSeg8K dataset [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: A random set of 6 generated image-mask pairs from the CaDISv2 dataset [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: A random set of 6 generated image-mask pairs from the Kvasir-SEG dataset [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: A random set of 6 generated image-mask pairs from the CholecInstanceSeg dataset [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: A random set of 6 generated image-mask pairs from the Endoscapes dataset [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: A random set of 6 generated image-mask pairs from the EndoVIS dataset [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: Comparison of anatomical semantic and boundary correctness: SimGen-generated masks versus SegFormer-predicted [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]
Figure 12
Figure 12. Figure 12: Qualitative ablation on feature dimension reduction [PITH_FULL_IMAGE:figures/full_fig_p008_12.png]
Figure 13
Figure 13. Figure 13: Qualitative results of SimGen across 3 complementary datasets in comparison with baselines. [PITH_FULL_IMAGE:figures/full_fig_p009_13.png]
Figure 14
Figure 14. Figure 14: SimGen’s utility on Segmentation. Model trained [PITH_FULL_IMAGE:figures/full_fig_p009_14.png]
Figure 15
Figure 15. Figure 15: Utility of SimGen generated data when trained on superset dataset with more data samples. [PITH_FULL_IMAGE:figures/full_fig_p010_15.png]
Figure 16
Figure 16. Figure 16: Utility of SimGen generated data when trained on [PITH_FULL_IMAGE:figures/full_fig_p010_16.png]
Figure 17
Figure 17. Figure 17: Examples of failure cases in generative model [PITH_FULL_IMAGE:figures/full_fig_p010_17.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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

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Reference graph

Works this paper leans on

35 extracted references · 21 canonical work pages · cited by 1 Pith paper

  1. [1]

    Surgical text- to-image generation,

    C. I. Nwoye, R. Bose, K. Elgohary, L. Arboit, G. Carlino, J. L. Lavanchy, P. Mascagni, and N. Padoy, “Surgical text- to-image generation,” arXiv preprint arXiv:2407.09230, 2024

    arXiv 2024

  2. [2]

    Navi- gating the synthetic realm: Harnessing diffusion-based models for laparoscopic text-to-image generation,

    S. Allmendinger, P. Hemmer, M. Queisner, I. Sauer, L. Müller, J. Jakubik, M. Vössing, and N. Kühl, “Navi- gating the synthetic realm: Harnessing diffusion-based models for laparoscopic text-to-image generation,”arXiv e-prints, pp. arXiv–2312, 2023

    work page 2023

  3. [3]

    Bora: Biomedical generalist video genera- tion model,

    W. Sun, X. You, R. Zheng, Z. Yuan, X. Li, L. He, Q. Li, and L. Sun, “Bora: Biomedical generalist video genera- tion model,” arXiv:2407.08944, 2024

    arXiv 2024

  4. [4]

    Surgen: Text-guided diffusion model for surgical video generation,

    J. Cho, S. Schmidgall, C. Zakka, M. Mathur, R. Shad, and W. Hiesinger, “Surgen: Text-guided diffusion model for surgical video generation,” arXiv preprint arXiv:2408.14028, 2024

    arXiv 2024

  5. [5]

    Ssis-seg: Simulation- supervised image synthesis for surgical instrument seg- mentation,

    E. Colleoni, D. Psychogyios, B. Van Amsterdam, F. Vas- concelos, and D. Stoyanov, “Ssis-seg: Simulation- supervised image synthesis for surgical instrument seg- mentation,” TMI, vol. 41, no. 11, pp. 3074–3086, 2022

    work page 2022

  6. [6]

    Min- max similarity: A contrastive semi-supervised deep learning network for surgical tools segmentation,

    A. Lou, K. Tawfik, X. Yao, Z. Liu, and J. Noble, “Min- max similarity: A contrastive semi-supervised deep learning network for surgical tools segmentation,” TMI, vol. 42, no. 10, pp. 2832–2841, 2023

    work page 2023

  7. [7]

    Deep learning methods for the detection and recognition of surgical tools and activities in laparo- scopic videos,

    C. I. Nwoye, “Deep learning methods for the detection and recognition of surgical tools and activities in laparo- scopic videos,” Ph.D. dissertation, Université de Stras- bourg, 2021

    work page 2021

  8. [8]

    Guided image generation for improved surgical image segmentation,

    E. Colleoni, R. S. Matilla, I. Luengo, and D. Stoy- anov, “Guided image generation for improved surgical image segmentation,” Medical Image Analysis, vol. 97, p. 103263, 2024

    work page 2024

Show all 35 references
  1. [9]

    Sur- gical action triplet detection by mixed supervised learn- ing of instrument-tissue interactions,

    S. Sharma, C. I. Nwoye, D. Mutter, and N. Padoy, “Sur- gical action triplet detection by mixed supervised learn- ing of instrument-tissue interactions,” in International Conference on Medical Image Computing and Computer- Assisted Intervention. Springer, 2023, pp. 505–514

    2023

  2. [10]

    Self-distillation for surgi- cal action recognition,

    A. Yamlahi, T. N. Tran, P. Godau, M. Schellenberg, D. Michael, F.-H. Smidt, J.-H. Nölke, T. J. Adler, M. D. Tizabi, C. I. Nwoye et al. , “Self-distillation for surgi- cal action recognition,” in International Conference on Medical Image Computing and Computer-Assisted Inter- v...

    2023

  3. [11]

    Surgitrack: Fine-grained multi-class multi-tool tracking in surgical videos,

    C. I. Nwoye and N. Padoy, “Surgitrack: Fine-grained multi-class multi-tool tracking in surgical videos,” arXiv preprint arXiv:2405.20333, 2024

    2024 arXiv

  4. [12]

    Denoising diffusion proba- bilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion proba- bilistic models,” NeurIPS, vol. 33, pp. 6840–6851, 2020. 11 SimGen: Simultaneous Image-Mask Generation GENERATIVE AI IN SURGERY

    2020

  5. [13]

    Cholecseg8k: a semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80,

    W.-Y . Hong, C.-L. Kao, Y .-H. Kuo, J.-R. Wang, W.- L. Chang, and C.-S. Shih, “Cholecseg8k: a semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80,” arXiv preprint arXiv:2012.12453, 2020

    2012 arXiv

  6. [14]

    2017 robotic instrument segmentation challenge,

    M. Allan, A. Shvets, T. Kurmann, Z. Zhang, R. Duggal, Y .-H. Su, N. Rieke, I. Laina, N. Kalavakonda, S. Bo- denstedt et al., “2017 robotic instrument segmentation challenge,” arXiv preprint arXiv:1902.06426, 2019

    2017 arXiv

  7. [15]

    The en- doscapes dataset for surgical scene segmentation, object detection, and critical view of safety assessment: Official splits and benchmark,

    A. Murali, D. Alapatt, P. Mascagni, A. Vardazaryan, A. Garcia, N. Okamoto, G. Costamagna, D. Mutter, J. Marescaux, B. Dallemagne, and N. Padoy, “The en- doscapes dataset for surgical scene segmentation, object detection, and critical view of safety assessment: Official splits ...

    2023 arXiv

  8. [16]

    Kvasir- seg: A segmented polyp dataset,

    D. Jha, P. H. Smedsrud, M. A. Riegler, P. Halvorsen, T. De Lange, D. Johansen, and H. D. Johansen, “Kvasir- seg: A segmented polyp dataset,” in MultiMedia model- ing. Springer, 2020, pp. 451–462

    2020

  9. [17]

    Cholecinstanceseg: A tool instance segmentation dataset for laparoscopic surgery,

    O. Alabi, K. K. Z. Toe, Z. Zhou, C. Budd, N. Raison, M. Shi, and T. Vercauteren, “Cholecinstanceseg: A tool instance segmentation dataset for laparoscopic surgery,” arXiv preprint arXiv:2406.16039, 2024

    2024 arXiv

  10. [18]

    2020 cataracts semantic seg- mentation challenge,

    I. Luengo, M. Grammatikopoulou, R. Mohammadi, C. Walsh, C. I. Nwoye, D. Alapatt, N. Padoy, Z.-L. Ni, C.- C. Fan, G.-B. Bian et al., “2020 cataracts semantic seg- mentation challenge,” arXiv preprint arXiv:2110.10965, 2021

    2020 arXiv

  11. [19]

    Road extraction by deep residual u-net,

    Z. Zhang, Q. Liu, and Y . Wang, “Road extraction by deep residual u-net,” IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 5, pp. 749–753, 2018

    2018

  12. [20]

    Segformer: Simple and efficient design for semantic segmentation with transformers,

    E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” NeurIPS, vol. 34, pp. 12 077–12 090, 2021

    2021

  13. [21]

    Auto-encoding variational bayes,

    D. P. Kingma, “Auto-encoding variational bayes,” arXiv:1312.6114, 2013

    2013 arXiv

  14. [22]

    Generative adversarial nets,

    I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y . Bengio, “Generative adversarial nets,” NeurIPS, vol. 27, 2014

    2014

  15. [23]

    Conditional generative adversarial nets,

    M. Mirza, “Conditional generative adversarial nets,” arXiv preprint arXiv:1411.1784, 2014

    2014 arXiv

  16. [24]

    A style-based genera- tor architecture for generative adversarial networks,

    T. Karras, S. Laine, and T. Aila, “A style-based genera- tor architecture for generative adversarial networks,” in CVPR, 2019, pp. 4401–4410

    2019

  17. [25]

    Denoising diffu- sion implicit models,

    J. Song, C. Meng, and S. Ermon, “Denoising diffu- sion implicit models,” arXiv preprint arXiv:2010.02502, 2020

    2010 arXiv

  18. [26]

    High-resolution image synthesis with latent diffusion models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in CVPR, 2022, pp. 10 684–10 695

    2022

  19. [27]

    Classifier-free diffusion guid- ance,

    J. Ho and T. Salimans, “Classifier-free diffusion guid- ance,” arXiv preprint arXiv:2207.12598, 2022

    2022 arXiv

  20. [28]

    Photorealistic text- to-image diffusion models with deep language under- standing,

    C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al., “Photorealistic text- to-image diffusion models with deep language under- standing,” NeurIPS, vol. 35, pp. 36 479–36 494, 2022

    2022

  21. [29]

    Video generation models as world simulators,

    T. Brooks, B. Peebles, C. Holmes, W. DePue, Y . Guo, L. Jing, D. Schnurr, J. Taylor, T. Luhman, E. Luhman, C. Ng, R. Wang, and A. Ramesh, “Video generation models as world simulators,”

  22. [30]

    Endora: Video generation models as endoscopy simulators,

    C. Li, H. Liu, Y . Liu, B. Y . Feng, W. Li, X. Liu, Z. Chen, J. Shao, and Y . Yuan, “Endora: Video generation models as endoscopy simulators,” in MICCAI. Springer, 2024, pp. 230–240

    2024

  23. [31]

    Rendezvous: Attention mechanisms for the recognition of surgical action triplets in endoscopic videos,

    C. I. Nwoye, T. Yu, C. Gonzalez, B. Seeliger, P. Mascagni, D. Mutter, J. Marescaux, and N. Padoy, “Rendezvous: Attention mechanisms for the recognition of surgical action triplets in endoscopic videos,” Medical Image Analysis, vol. 78, p. 102433, 2022

    2022

  24. [32]

    Cadis: Cataract dataset for surgical rgb- image segmentation,

    M. Grammatikopoulou, E. Flouty, A. Kadkhodamoham- madi, G. Quellec, A. Chow, J. Nehme, I. Luengo, and D. Stoyanov, “Cadis: Cataract dataset for surgical rgb- image segmentation,” Medical Image Analysis, vol. 71, p. 102053, 2021

    2021

  25. [33]

    Unsupervised representation learning with deep convolutional generative adversarial networks,

    A. Radford, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv:1511.06434, 2015

    2015 arXiv

  26. [34]

    Image-to- image translation with conditional adversarial networks,

    P. Isola, J.-Y . Zhu, T. Zhou, and A. A. Efros, “Image-to- image translation with conditional adversarial networks,” in CVPR, 2017, pp. 1125–1134. 12

    2017

  27. [2024]

    Available: https://openai.com/research/ video-generation-models-as-world-simulators

    [Online]. Available: https://openai.com/research/ video-generation-models-as-world-simulators

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.