BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization

Adri\'an Colomer; Alejandro Golfe; Natalia P. Garc\'ia-de-la-puente; Valery Naranjo

REVIEW 5 major objections 5 minor 25 references

BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that a latent diffusion model conditioned only on binary blastocyst-outcome labels can generate oocyte images that preserve the morphological cues separating blastocyst-forming from non-forming oocytes, and that it…

desk verdict A plausible but unproven conditional oocyte generator: the FID table shows better global realism than a self-made GAN, yet the paper never tests whether the blastocyst label actually steers the morphology, a gap the authors concede. read the letter →

arxiv 2506.13843 v1 pith:PALRSB4X submitted 2025-06-16 q-bio.QM

Alejandro Golfe , Natalia P. García-de-la-puente , Adrián Colomer , Valery Naranjo This is my paper · ORCID

classification q-bio.QM

keywords latentdiffusionmodelsyntheticoocyteimagesblastocystinvitrofertilizationdataaugmentationconditionalimagesynthesisgenerativeembryoassessment

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

Accurately identifying which oocytes will reach the blastocyst stage is central to IVF, but annotated oocyte image datasets are small. The paper proposes BlastDiffusion, a latent diffusion model that generates synthetic oocyte images conditioned on a binary label: whether the oocyte later formed a blastocyst. The authors claim it produces more realistic and more class-distinct images than a GAN baseline they trained for the same task, with a global FID of $94.32$ versus $232.73$, and better LPIPS and SSIM scores. If correct, this makes conditional diffusion models a practical route for augmenting scarce embryo data and for automated embryo assessment.

What carries the argument

The central object is a Latent Diffusion Model: a diffusion model trained to denoise compressed latent representations produced by a pretrained VAE, rather than raw pixels; a decoder then maps denoised latents back to images. Conditioning is carried by an embedding vector $y$ for the binary outcome (blastocyst or not), passed through a learnable transformation $\tau_\theta$ into a conditioning vector $c$, which is injected through cross-attention layers in the denoising U-Net. A pretrained object detector first crops each oocyte from the microscope frame. This machinery is doing the work of making class-conditioned synthesis tractable on only 2,217 images, because the diffusion process operates in a lower-dimensional space while the conditioning signal steers it toward outcome-specific morphology.

What would settle it

Measure reconstruction fidelity of the VAE on real oocyte images, comparing $D(E(x))$ with $x$ per outcome class and within the zona pellucida and perivitelline regions; if those regions show systematically higher reconstruction error or smoothing than the rest of the image, the latent representation is discarding the very morphology the generation claim depends on. A complementary check is to train an independent classifier on real oocytes and test it on synthetic BlastDiffusion images (or vice versa): if the class-specific morphology is real, transfer accuracy should be well above chance.

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Extended reading notes

Core claim

The paper's central claim is that running a denoising diffusion process in the latent space of a pretrained VAE, with a class-embedding vector injected through cross-attention, is enough to synthesize oocyte images that separate blastocyst-stage outcomes. It also presents itself as the first latent diffusion model for conditional oocyte image generation based on developmental viability. On the paper's own evaluation, BlastDiffusion achieves a global Frechet Inception Distance of $94.32$ against $232.73$ for Blastocyst-GAN, lower LPIPS values ($0.2877$ vs $0.394$), and higher SSIM values ($0.4462$ vs $0.310$). Qualitative inspection shows the generated blastocyst-class oocytes have smoother, more homogeneous cytoplasm, cleaner perivitelline space, and a more regular zona pellucida, while the non-blastocyst class shows granular cytoplasm and adherent debris; the authors read this as evidence the model faithfully reproduces morphological cues that distinguish the two outcomes.

Load-bearing premise

The load-bearing premise is that the pretrained VAE, which was trained on ordinary natural images, compresses oocyte microscope images without losing the fine zona pellucida, perivitelline-space, and cytoplasm details that separate blastocyst-forming from non-forming oocytes; if the latent bottleneck discards those details, no amount of diffusion can recover them.

Editorial extensions

If this is right

BlastDiffusion can generate additional oocyte images for either outcome class, which can be used as targeted augmentation for training embryo-selection classifiers, a use the paper motivates explicitly.
A binary-conditioned latent diffusion model can capture morphological differences between blastocyst-forming and non-forming oocytes, including cytoplasm homogeneity, perivitelline-space debris, and zona pellucida regularity.
On a small oocyte dataset, a diffusion-based conditional generator can outperform a progressive-growing conditional GAN by a wide FID margin.
Scaling the dataset and enriching the class condition with text annotations or molecular markers should improve image specificity and expand the range of oocyte phenotypes the model represents, a direction the paper states for future work.

Reading between the lines

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

Editorial inference: because the conditioning is only a binary label, the model could be latching onto global image correlates such as focus, illumination, or zona thickness rather than the biological signals; a reader should test how well generated class differences survive when real images are matched for such global statistics.
Editorial inference: the paper's future-work suggestion to validate synthetic images with an independent embryo-quality classifier is testable now: train a classifier on real images, then measure whether adding BlastDiffusion images changes its accuracy on held-out real images.
Editorial inference: the untested dependence on the pretrained VAE can be probed directly by measuring reconstruction fidelity on oocyte images, stratified by the two outcome classes; if the zona pellucida and perivitelline space are systematically blurred, generation cannot be faithful.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The genuinely new thing here is applying a latent diffusion model to oocyte images with class conditioning on blastocyst outcome. That specific combination looks absent from prior work, and the paper is honest about it. What it does well: patient-level train/test split, IRB approval, clear description of the pipeline, and a consistent improvement over its own GAN baseline across FID, LPIPS, and SSIM. The qualitative figures also show visible differences between the two generated classes, which is more than nothing.

The soft spots are real and they sit on the load-bearing claim. Table 1 gives FID, LPIPS, and SSIM, but those are unconditional or pairwise similarities; none of them verifies that the conditioning label controls what is generated. A model that ignores the label and draws from a generic oocyte distribution could score similarly. The class-level FIDs (101.2 and 105.5) are actually worse than the pooled FID (94.3), and there are no confidence intervals, so the conditional advantage is not demonstrated. The paper itself says the decisive validation, an independent embryo-quality classifier, is future work. That is a conceded limitation, not a hidden one, but it means the central promise of outcome-specific augmentation is unsupported.

Two other concerns are minor but worth naming. The pretrained Stable Diffusion VAE trained on LAION-5B is used without any check of reconstruction fidelity on oocyte microscopy; if the latent bottleneck throws away perivitelline detail, the whole downstream generation is compromised. And the absolute FID of 94 is high, which undercuts the word 'realistic.' The baseline is also self-designed, so the comparison, while internally consistent, does not position the method against existing published embryo generators.

None of this is fatal to the idea. The paper is a reasonable proof-of-concept with a clear direction. The missing experiments are testable and the authors know what they are. Who is this for? Researchers working on generative models for reproductive medicine or on conditional image synthesis with small clinical datasets. A serious referee could turn this into a solid paper by asking for a conditional fidelity check, a VAE reconstruction evaluation, and a downstream augmentation experiment.

My recommendation: send it to peer review. It deserves referee time, with the expectation of major revision. The core flaw is incomplete evidence, not a wrong method.

Referee Report

5 major / 5 minor

Summary. The manuscript introduces BlastDiffusion, a conditional latent diffusion model that generates synthetic oocyte images conditioned on whether the oocyte reaches the blastocyst stage. The model uses a pretrained Stable Diffusion VAE for latent encoding and a cross-attention conditioning mechanism with a class embedding. The authors compare BlastDiffusion with Blastocyst-GAN, a conditional progressive GAN they trained on the same dataset, and report lower FID (94.32 vs 232.73), lower LPIPS, and higher SSIM. They also provide qualitative comparisons of real and generated oocytes. The stated motivation is to address data scarcity in IVF embryo assessment by enabling data augmentation and automated embryo assessment.

Significance. The potential significance of a well-validated conditional oocyte generator is real: blastocyst-stage morphology is used in IVF embryo selection, and annotated oocyte data are scarce. The paper's strengths include a patient-level data split to avoid leakage, an IRB-approved protocol, and transparent reporting of training hyperparameters for BlastDiffusion. The main quantitative comparison is against a baseline trained by the authors, and the reported FID/LPIPS/SSIM improvements support only the relative claim that the method beats this particular GAN baseline. The higher-level claims in the abstract and conclusion—that the model reproduces outcome-specific morphological cues and is useful for data augmentation—are not currently supported by the evidence, because no experiment directly verifies that the conditioning label controls generated content and no downstream augmentation experiment is performed. If the conditional-generation validation experiments are added, the work would be a useful methodological contribution to reproductive imaging; in its current form the evidence is suggestive but incomplete.

major comments (5)

[Section 5.2, Table 1] The reported metrics do not establish that the conditioning label controls the generated morphology. FID, LPIPS, and SSIM are distributional or pairwise similarity scores; the class-level FID values compare each generated class to the corresponding real class, but a generator that ignores the label and samples from the pooled mixture can still produce these numbers. I request a label-consistency test: for example, train a blastocyst predictor on real images and measure its accuracy on generated images conditioned on each label, and compute a conditional FID between generated-onset and generated-non-onset images. Without such a test, the claim in Section 6 that the model 'faithfully reproduces morphological cues' is unsupported.
[Section 4.1] The pretrained Stable Diffusion VAE is applied with no fine-tuning and no validation of reconstruction quality on oocyte images. The VAE was trained on LAION-5B natural images; oocyte microscopy images contain fine perivitelline-space and zona-pellucida detail that may be discarded at the latent bottleneck. Please report VAE reconstruction error (e.g., PSNR/SSIM/FID between original and reconstructed real oocytes) and show visual comparisons, or fine-tune the VAE on oocyte data. This is load-bearing because the diffusion model operates entirely in the VAE latent space; any information lost at encoding cannot be recovered by the denoiser.
[Section 6] The paper itself states that validation by an independent embryo-quality classifier and prospective clinical evaluation are future work. This is an appropriate acknowledgment, but it is in tension with the abstract's claim that the model 'synthesizes realistic oocyte images' and 'faithfully reproduces morphological cues.' The central data-augmentation motivation is also untested: no experiment shows that augmenting a classifier's training set with BlastDiffusion images improves classification accuracy. Please either add such an experiment or substantially soften the claims in the abstract and conclusion.
[Section 5.1 and Table 1] The FID comparison lacks key statistical and procedural details. No confidence intervals, number of generated images per class, or sampling parameters (number of diffusion denoising steps, classifier-free guidance scale, if used) are reported. Also, the Total FID for BlastDiffusion (94.32) is lower than both per-class FID values (101.17 and 105.50), which needs an explanation; it may indicate that the per-class comparison is underpowered or that the class-conditioned distributions are not well separated. This limits the interpretability of the central quantitative claim.
[Section 5.3] The qualitative evaluation is performed on a small set of selected examples and is not blinded or quantified. The arrows in Figure 3 highlight features that the authors associate with classes, but there is no inter-rater agreement or test that the generated class-conditioned sets differ on these features. Please add a quantitative morphological analysis (e.g., measures of perivitelline-space width or cytoplasm texture) or a blinded rating by embryologists.

minor comments (5)

[Section 5.1] Please provide the architecture and training details for Blastocyst-GAN (e.g., learning rate, number of epochs, number of generated samples) to make the baseline comparison reproducible.
[Section 5.1 and Table 1] LPIPS and SSIM values are reported as mean ± std, but no significance test or number of matched pairs is given; please clarify how these pairwise metrics were computed.
[Section 4.1, Eq. (1)] The noise schedule coefficients α_t are introduced without specifying whether they are cumulative products or per-step values; please clarify the schedule definition.
[Author footnote] The author footnote contains a typo: 'Correspondng' should be 'Corresponding'.
[Table 1 caption] The caption has an extra space: 'T able 1' should be 'Table 1'.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: evaluation uses external FID/LPIPS/SSIM metrics, the GAN baseline is trained from scratch, and no load-bearing claim reduces to a fitted input or self-citation.

full rationale

The paper's central claim is that BlastDiffusion generates realistic oocyte images conditioned on blastocyst outcome, with FID 94.32 versus Blastocyst-GAN's 232.73. The evaluation metrics are externally defined (Inception-v3 embeddings for FID, learned perceptual features for LPIPS, and structural SSIM), and the comparison baseline is explicitly trained from scratch on the oocyte dataset rather than being a reused result. The conditioning mechanism is a standard class-embedding injected through cross-attention, not a quantity defined in terms of the outcome it is supposed to predict. No equation in the paper defines a predicted quantity as the fitted parameter or as the conditioning label by construction. The cited self-works are not load-bearing: ProGleason-GAN [15] is used only as an architectural starting point for the baseline and is retrained, and the YOLO detector reference [19] supports preprocessing, not the generative claim. The paper itself acknowledges that the biologically meaningful conditioning claim still needs independent validation ('synthetic images ought to be validated by an independent embryo-quality classifier and, eventually, through prospective clinical evaluation'), which is an admitted limitation rather than circular reasoning. One minor note: the training configuration mentions a perceptual loss weight of 1 while LPIPS is reported as a metric, but the paper does not identify the training perceptual loss as LPIPS, and the headline FID is unaffected. Overall, no circular step can be exhibited from the manuscript text.

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

No new physical entities or fitted scientific constants are introduced. The model relies on established diffusion theory and clinical outcome labels; the main assumptions are domain-transfer of the pretrained VAE, learnability of the outcome from images, validity of ImageNet-trained metrics for oocyte images, and the accuracy of the YOLO crop.

free parameters (5)

learning_rate = 0.00005
Chosen by hand for the BlastDiffusion Adam optimizer (Section 5.1); the reported FID depends on this choice.
batch_size = 24
Set in Section 5.1; affects training dynamics of the diffusion model.
num_epochs = 4000
Training duration in Section 5.1; stopping criterion is not justified.
conditional_dropout_probability = 0.2
Class-conditioning dropout used during training (Section 5.1); affects conditional fidelity.
perceptual_loss_weight = 1
Weight for the perceptual loss term (Section 5.1); relative weighting vs. diffusion loss is not tuned or ablated.

assumptions (4)

domain assumption The pretrained Stable Diffusion VAE, trained on LAION-5B natural images, faithfully encodes and decodes oocyte microscopy images without fine-tuning.
Section 4.1 adopts a LAION-5B VAE as the latent backbone and never reports reconstruction error on oocyte images. If the VAE loses fine oocyte structures, the generated images cannot preserve the morphological cues the paper claims.
domain assumption The binary clinical outcome label (blastocyst vs. non-blastocyst) is learnable from oocyte image appearance at the time of ICSI.
The conditioning signal y encodes this outcome; if oocyte morphology at this stage does not actually distinguish future blastocyst formation, the class-conditional generation differences are spurious. The paper cites its own prior work [19] for this premise.
domain assumption Inception-v3-based FID, LPIPS, and SSIM are meaningful image-quality metrics for oocyte microscopy images.
These metrics were designed for natural images; no validation demonstrates they reflect clinically relevant oocyte morphology. The paper uses them as the only quantitative evidence of realism.
domain assumption The YOLOv8n oocyte detector produces crops that preserve all diagnostically relevant oocyte structures.
Section 3.1 uses a YOLO model trained on 61 manually labelled images to crop oocytes; errors in cropping would remove or distort perivitelline space and zona pellucida features that the qualitative analysis depends on.

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Cite this review

Pith. "Pith review of BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization." pith.science (2026). https://pith.science/paper/PALRSB4X

@misc{pith2026250613843,
  author       = {Pith},
  title        = {Pith review of: BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PALRSB4X}},
  note         = {Machine review of arXiv:2506.13843}
}

read the original abstract

Accurately identifying oocytes that progress to the blastocyst stage is crucial in reproductive medicine, but the limited availability of annotated high-quality embryo images presents challenges for developing automated diagnostic tools. To address this, we propose BlastDiffusion, a generative model based on Latent Diffusion Models (LDMs) that synthesizes realistic oocyte images conditioned on developmental outcomes. Our approach utilizes a pretrained Variational Autoencoder (VAE) for latent space representation, combined with a diffusion process to generate images that distinguish between oocytes that reach the blastocyst stage and those that do not. When compared to Blastocyst-GAN, a GAN-based model we trained for this task, BlastDiffusion achieves superior performance, with a global Frechet Inception Distance (FID) of 94.32, significantly better than Blastocyst-GAN's FID of 232.73. Additionally, our model shows improvements in perceptual (LPIPS) and structural (SSIM) similarity to real oocyte images. Qualitative analysis further demonstrates that BlastDiffusion captures key morphological differences linked to developmental outcomes. These results highlight the potential of diffusion models in reproductive medicine, offering an effective tool for data augmentation and automated embryo assessment.

Figures

Figures reproduced from arXiv: 2506.13843 by the authors.

**Figure 1.** Overview of BlastDiffusion framework. A Variational Autoencoder (VAE) encodes the input image into a latent space, where a LDM refines noisy representations into realistic embeddings. The process is conditioned on developmental outcome, allowing the generation of biologically relevant synthetic images. extractor. Given an input image x ∈ R H×W×C , the VAE encoder E compresses it into a lower-dimensional latent rep… view at source ↗

**Figure 2.** Real oocyte images. The first row (a) corresponds to oocytes that did not reach the blastocyst stage, while the second row (b) shows oocytes that successfully developed into blastocysts. (a) (b) [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** Generated oocytes using BlastDiffusion. The first row (a) consists of generated oocytes that did not reach the blastocyst stage, while the second row (b) represents those predicted to develop into blastocysts. Specific features are highlighted with arrows. In conclusion, the conditional synthesis of BlastDiffusion shows significant improvements in oocyte generation, both in terms of visual realism and the accurate… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Generated oocytes using Blastocyst-GAN. The first row (a) represents generated oocytes that did not reach the blastocyst stage, while the second row (b) corresponds to those predicted to develop into blastocysts. BlastDiffusion produces more realistic images and is a…

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Reference graph

Works this paper leans on

25 extracted references · 21 canonical work pages

[1]

Culture and selection of viable blastocysts: a feasible proposition for human ivf?

D. K. Gardner and M. Lane, “Culture and selection of viable blastocysts: a feasible proposition for human ivf?”Human reproduction update, vol. 3, no. 4, pp. 367–382, 1997

work page 1997
[2]

A prospective randomized trial of blastocyst culture and transfer in in-vitro fertilization

D. K. Gardner, W. B. Schoolcraft, L. Wagley, T. Schlenker, J. Stevens, and J. Hesla, “A prospective randomized trial of blastocyst culture and transfer in in-vitro fertilization.”Human reproduction (Oxford, England), vol. 13, no. 12, pp. 3434–3440, 1998

work page 1998
[3]

Embryo selection through artificial intelligence versus embryologists: a systematic review,

M. Salih, C. Austin, R. Warty, C. Tiktin, D. Rolnik, M. Momeni, H. Rezatofighi, S. Reddy, V. Smith, B. Vollenhovenet al., “Embryo selection through artificial intelligence versus embryologists: a systematic review,” Human Reproduction Open, vol. 2023, no. 3, p. hoad031, 2023

work page 2023
[4]

Auto-encoding variational bayes,

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

arXiv 2013
[5]

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,”Ad- vances in neural information processing systems, vol. 27, 2014

work page 2014
[6]

Diffusion models in vision: A survey,

F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion models in vision: A survey,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 10850–10869, 2023

work page 2023
[7]

Inter-laboratory agreement on embryo classification and clinical decision: conventional morphological assessment vs. time lapse,

L. Martínez-Granados, M. Serrano, A. González-Utor, N. Ortíz, V. Bada- joz, E. Olaya, N. Prados, M. Boada, J. A. Castilla, and S. I. G. in Qual- ity of ASEBIR (Spanish Society for the Study of Reproductive Biology), “Inter-laboratory agreement on embryo classification and clinical decision: conventional morphological assessment vs. time lapse,”PLoS One, v...

work page 2017
[8]

Artificial intel- ligence in ivf laboratories: Elevating outcomes through precision and effi- ciency,

Y. Hew, D. Kutuk, T. Duzcu, Y. Ergun, and M. Basar, “Artificial intel- ligence in ivf laboratories: Elevating outcomes through precision and effi- ciency,”Biology, vol. 13, no. 12, p. 988, 2024

work page 2024

Show all 25 references

[9]

Enhance image classification via inter-class image mixup with diffusion model,

Z. Wang, L. Wei, T. Wang, H. Chen, Y. Hao, X. Wang, X. He, and Q. Tian, “Enhance image classification via inter-class image mixup with diffusion model,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 17223–17233

2024
[10]

Latent diffusion models for structural component design,

E. Herron, J. Rade, A. Jignasu, B. Ganapathysubramanian, A. Balu, S. Sarkar, and A. Krishnamurthy, “Latent diffusion models for structural component design,”Computer-Aided Design, vol. 171, p. 103707, 2024

2024
[11]

Pathldm: Text conditioned latent diffusion model for histopathology,

S. Yellapragada, A. Graikos, P. Prasanna, T. Kurc, J. Saltz, and D. Sama- ras, “Pathldm: Text conditioned latent diffusion model for histopathology,” inProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024, pp. 5182–5191

2024
[12]

Embryo 2.0: Merging synthetic 14 F. Author et al. andrealdataforadvancedaipredictions,

O. Presacan, A. Dorobantiu, V. Thambawita, M. A. Riegler, M. H. Stensen, M. Iliceto, A. C. Aldea, and A. Sharma, “Embryo 2.0: Merging synthetic 14 F. Author et al. andrealdataforadvancedaipredictions,”arXiv preprint arXiv:2412.01255, 2024

2024 arXiv
[13]

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,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition,2022, pp. 10684–10695

2022
[14]

Diffusion models beat gans on image synthesis,

P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” Advances in neural information processing systems, vol. 34, pp. 8780–8794, 2021

2021
[15]

Progleason-gan: Conditional progressive growing gan for pro- static cancer gleason grade patch synthesis,

A. Golfe, R. Del Amor, A. Colomer, M. A. Sales, L. Terradez, and V. Naranjo, “Progleason-gan: Conditional progressive growing gan for pro- static cancer gleason grade patch synthesis,”Computer Methods and Pro- grams in Biomedicine, vol. 240, p. 107695, 2023

2023
[16]

Diffmix: Diffusion model-based data synthesis for nuclei segmentation and classification in imbalanced pathology image datasets,

H.-J. Oh and W.-K. Jeong, “Diffmix: Diffusion model-based data synthesis for nuclei segmentation and classification in imbalanced pathology image datasets,” inInternational Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2023, pp. 337–345

2023
[17]

Yolov8: A novel object detection algorithm with enhanced performance and robustness,

R. Varghese and M. Sambath, “Yolov8: A novel object detection algorithm with enhanced performance and robustness,” in2024 International Confer- ence on Advances in Data Engineering and Intelligent Computing Systems (ADICS). IEEE, 2024, pp. 1–6

2024
[18]

Yolo for medical object detection (2018–2024),

A. Soni and A. Rai, “Yolo for medical object detection (2018–2024),” in 2024 IEEE 3rd International Conference on Electrical Power and Energy Systems (ICEPES). IEEE, 2024, pp. 1–7

2018
[19]

Unsupervised prediction of blastocyst de- velopment from oocyte images,

N. P. García-de-la Puente, E. Paya, L. Murria, M. López-Pérez, M. Meseguer, and V. Naranjo, “Unsupervised prediction of blastocyst de- velopment from oocyte images,” inDecision Science Alliance International Summer Conference. Springer, 2024, pp. 199–209

2024
[20]

Laion-5b: An open large-scale dataset for training next generation image-text mod- els,

C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsmanet al., “Laion-5b: An open large-scale dataset for training next generation image-text mod- els,”Advances in neural information processing systems, vol. 35, pp. ...

2022
[21]

Progressive growing of gans for improved quality, stability, and variation,

T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,”arXiv preprint arXiv:1710.10196, 2017

2017 arXiv
[22]

Improved training of wasserstein gans,

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,”Advances in neural information processing systems, vol. 30, 2017

2017
[23]

Gans trained by a two time-scale update rule converge to a local nash equilibrium,

M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,”Advances in neural information processing systems, vol. 30, 2017

2017
[24]

Rethinking the inception architecture for computer vision,

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818–2826. BlastDiffusion: Latent Diffusion for Synthetic Embryo...

2016
[25]

Perceptual losses for real-time style transferandsuper-resolution,

J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transferandsuper-resolution,” inComputer Vision–ECCV 2016: 14th Euro- pean Conference, Amsterdam, The Netherlands, October 11-14, 2016, Pro- ceedings, Part II 14. Springer, 2016, pp. 694–711

2016

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Culture and selection of viable blastocysts: a feasible proposition for human ivf?

D. K. Gardner and M. Lane, “Culture and selection of viable blastocysts: a feasible proposition for human ivf?”Human reproduction update, vol. 3, no. 4, pp. 367–382, 1997

work page 1997

[2] [2]

A prospective randomized trial of blastocyst culture and transfer in in-vitro fertilization

D. K. Gardner, W. B. Schoolcraft, L. Wagley, T. Schlenker, J. Stevens, and J. Hesla, “A prospective randomized trial of blastocyst culture and transfer in in-vitro fertilization.”Human reproduction (Oxford, England), vol. 13, no. 12, pp. 3434–3440, 1998

work page 1998

[3] [3]

Embryo selection through artificial intelligence versus embryologists: a systematic review,

M. Salih, C. Austin, R. Warty, C. Tiktin, D. Rolnik, M. Momeni, H. Rezatofighi, S. Reddy, V. Smith, B. Vollenhovenet al., “Embryo selection through artificial intelligence versus embryologists: a systematic review,” Human Reproduction Open, vol. 2023, no. 3, p. hoad031, 2023

work page 2023

[4] [4]

Auto-encoding variational bayes,

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

arXiv 2013

[5] [5]

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,”Ad- vances in neural information processing systems, vol. 27, 2014

work page 2014

[6] [6]

Diffusion models in vision: A survey,

F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion models in vision: A survey,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 10850–10869, 2023

work page 2023

[7] [7]

Inter-laboratory agreement on embryo classification and clinical decision: conventional morphological assessment vs. time lapse,

L. Martínez-Granados, M. Serrano, A. González-Utor, N. Ortíz, V. Bada- joz, E. Olaya, N. Prados, M. Boada, J. A. Castilla, and S. I. G. in Qual- ity of ASEBIR (Spanish Society for the Study of Reproductive Biology), “Inter-laboratory agreement on embryo classification and clinical decision: conventional morphological assessment vs. time lapse,”PLoS One, v...

work page 2017

[8] [8]

Artificial intel- ligence in ivf laboratories: Elevating outcomes through precision and effi- ciency,

Y. Hew, D. Kutuk, T. Duzcu, Y. Ergun, and M. Basar, “Artificial intel- ligence in ivf laboratories: Elevating outcomes through precision and effi- ciency,”Biology, vol. 13, no. 12, p. 988, 2024

work page 2024