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REVIEW 5 major objections 6 minor 1 cited by

Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models

T0 review · 5 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Fetal plane classifiers trained first on diffusion-generated ultrasound images and then fine-tuned on real images outperform classifiers trained on real images alone.

desk verdict A useful demonstration of diffusion-based augmentation for fetal ultrasound, but the attribution of gains to diffusion itself is not supported by the authors' own ablation. read the letter →

arxiv 2501.15248 v2 pith:ACILB7R2 submitted 2025-01-25 cs.CV

Yueying Tian , Elif Ucurum , Xudong Han , Rupert Young , Chris Chatwin , Philip Birch This is my paper · ORCID
classification cs.CV
keywords diffusionmodelsdataaugmentationfetalultrasoundimageclassificationsyntheticmedicalimagespretrainingandfine-tuningscarcityFETAL_PLANES_DB
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 sets out to show that diffusion-model-generated synthetic fetal ultrasound images can serve as a training resource that improves classifier accuracy beyond what real images alone provide. The authors generate 60,000 class-conditional synthetic images from the FETAL_PLANES_DB training set, pretrain five different classifiers on a 30,000- or 60,000-image synthetic corpus, and then fine-tune on the real images. Across every classifier they test, the pretrain-then-fine-tune recipe yields higher test accuracy than training only on the real data, with the best single model reaching 93.2% and a majority-vote ensemble reaching 94.91%. The gains are most visible for the fetal abdomen and fetal femur classes, which have the fewest real training examples.

What carries the argument

The machinery that carries the argument is a classifier-guided diffusion model: a denoising diffusion probabilistic model whose reverse denoising process is steered by a half-U-Net classifier trained alongside it, so that sampling conditioned on a label produces images of the corresponding fetal plane. Two rounds of generation produce 10,000 synthetic images per class (60,000 total), and the downstream gain comes from the two-stage training recipe that first pretrains each classifier on that synthetic corpus and then fully fine-tunes it on the 9,923 real training images. The consistency of the gain across five architecturally diverse classifiers, and across two synthetic corpus sizes, is what the paper offers as evidence that the synthetic data, not any single classifier, is doing the work.

What would settle it

A control experiment could settle the question: train each classifier for 400 epochs on the 9,923 real images alone to match the total training budget, or pretrain on 60,000 traditionally augmented real images (rotations, flips, crops) before the same fine-tuning; if either control matches or beats the reported pretrain-plus-fine-tune accuracy, the attribution of the gain to diffusion-generated synthetic data would be refuted.

Watch

Extended reading notes

Core claim

The paper's central claim is that a classifier-guided diffusion model trained on the real fetal ultrasound training split can generate enough class-labeled synthetic images that pretraining a downstream classifier on them, followed by fine-tuning on the real data, outperforms training on the real data alone. This claim is tested with ResNet50, DenseNet169, ViT_b_32, Swin_t, and MedMamba on the FETAL_PLANES_DB benchmark. In the main comparison, the 60,000-synthetic-image pretraining plus fine-tuning row beats the Real-Data-Only row for every classifier (for example, ResNet50 improves from 92.0% to 93.2% test accuracy). The authors also report that increasing the synthetic corpus from 30,000 to 60,000 images consistently improves the final accuracy, and that the benefit is concentrated in classes with the smallest real training sets.

Load-bearing premise

The paper's conclusion depends on the assumption that the accuracy gain comes from the diffusion-generated images themselves and not from the fact that the proposed training procedure simply runs many more optimization steps on a much larger pretraining corpus than the real-data-only baseline.

Editorial extensions

If this is right

  • If the claim holds, any medical imaging domain with a small annotated set can raise classifier accuracy by pretraining on a large diffusion-generated corpus and then fine-tuning on the real images, without acquiring new real labels.
  • Because the biggest gains appear in classes with the fewest real examples, the recipe offers a targeted remedy for class imbalance as well as overall data scarcity.
  • The reported trend that 60,000 synthetic images beat 30,000 implies that generating more synthetic data may keep improving downstream accuracy at least up to the tested scale.
  • The authors' plan to release the 60,000-image synthetic dataset would give other researchers a ready-made pretraining resource for fetal ultrasound classification.

Reading between the lines

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

  • The paper's own ablation shows that removing classifier guidance slightly improves downstream accuracy, so a simpler unguided diffusion model may be a cheaper and equally effective generator for this application.
  • Because the main comparison does not equalize total optimization steps between the real-data baseline and the synthetic-pretraining recipe, the diffusion-specific contribution remains entangled with extra training effort; a matched-budget control would likely change how the headline result is read.
  • The same pretrain-on-synthetic, fine-tune-on-real strategy should transfer to other ultrasound tasks such as biometry plane identification or anomaly detection, and to other modalities like MRI and CT where annotated data are similarly scarce.
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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

5 major / 6 minor

Summary. The manuscript proposes using a classifier-guided diffusion model trained on the FETAL_PLANES_DB training split to generate 60,000 synthetic fetal ultrasound images (30,000 in each of two rounds). The authors then pretrain five classifiers (ResNet50, DenseNet169, ViT_b_32, Swin_t, MedMamba) on the synthetic images and fine-tune them on the real training images, reporting that this two-stage procedure yields higher test accuracy than training on real images alone. The paper also includes visual and FID-based quality assessment of the synthetic images, confusion-matrix analyses, a majority-vote ensemble, comparisons to two prior fetal-plane classification methods, and three ablation studies. The central claim is that diffusion-generated synthetic images improve downstream classification accuracy relative to training with real data only.

Significance. If the central claim were established, the work would provide a practical recipe for mitigating data scarcity in fetal ultrasound classification and would contribute a large public synthetic dataset as a community resource. The paper has some genuine strengths: it evaluates five diverse classifier architectures, reports consistent directional improvements across them, and releases (or announces intent to release) the synthetic images. However, as it stands, the attribution of the gains to diffusion-model-generated content is not supported because the main comparison confounds data source with dataset size and optimization budget, and the paper's own ablation (Table 12) shows that pretraining on traditional augmentations matches or exceeds pretraining on diffusion images for four of five classifiers. The evaluation protocol also selects checkpoints on the test set and provides no error bars, so the reported numbers should be read as optimistic upper bounds. With additional controls, matched training budgets, and a corrected evaluation protocol, the underlying question is worth pursuing, but the current manuscript does not yet justify its headline conclusion.

major comments (5)
  1. [§5.2.1, Tables 5–9] The main comparison confounds the data source with the number of training iterations and the pretraining corpus size. 'Real Data Only' trains for 200 epochs on 9,923 real images, while 'Pretraining + Fine-Tuning' trains for 200 epochs on 30,000 or 60,000 synthetic images and then fine-tunes for 200 more epochs on the real data. The observed test-accuracy gains could therefore come from the additional optimization budget or the larger pretraining set rather than from the diffusion-model-specific distribution. A matched control, such as training 'Real Data Only' for 400 epochs, or pretraining on 30,000/60,000 traditional augmentations of the real images followed by the same fine-tuning and checkpoint-selection protocol, is needed before one can attribute the improvement to diffusion-generated content.
  2. [§5.3.1, Table 12] The ablation intended to control for traditional augmentation actually undermines the diffusion-specific claim. In the rows 'Pretraining (30000 traditional augmentation) + Fine-Tuning (9923 real)' versus 'Pretraining (30000 synthetic images) + Fine-Tuning (9923 real)', the traditional-augmentation pretraining achieves higher test accuracy for four of the five classifiers (ResNet50 93.8 vs. 92.5; DenseNet169 93.7 vs. 92.7; Swin_t 92.7 vs. 91.5; MedMamba 93.0 vs. 92.1), with only ViT_b_32 showing the opposite (88.2 vs. 89.6). This pattern is stronger evidence that the benefit arises from pretraining on a larger, more varied corpus than from the specific synthetic distribution emitted by the diffusion model. The paper's stated interpretation of Table 12 is therefore not supported by its own numbers, and the central claim should be revised or the experiments redesigned.
  3. [§4.3] The evaluation protocol selects checkpoints using the test set: 'we saved the best training and test accuracy during the training procedure.' This is a test-set leak, since the reported test accuracy is the maximum over 200 epochs rather than the accuracy of a fixed model chosen without looking at the test labels. No validation set or early-stopping criterion is described, and no standard errors or multiple seeds are reported, so the magnitude of the claimed improvements (e.g., 90.8% vs. 75.5% for ViT_b_32 in Table 5) is not trustworthy as an unbiased estimate. The authors should hold out a validation split for checkpoint selection and report mean and standard deviation over at least three independent runs.
  4. [§5.2.3, Table 10 vs. Tables 5–9] Table 10 reports test accuracies that are inconsistent with the corresponding entries in Tables 5–9. For example, Table 10 lists ResNet50 at 93.94%, DenseNet169 at 94.07%, Swin_t at 93.10%, MedMamba at 93.50%, and Vit_b at 90.15%, while the closest rows in Tables 5–9 ('Pretraining (60000 synthetic images) + Fine-Tuning') give 93.2%, 92.8%, 93.1%, 92.4%, and 90.8%, respectively. The caption of Table 10 says 'the Final Training Strategy: Pretraining with Combined Synthetic Data and Fine-Tuning,' but the relationship between this strategy and the 30k/60k pretraining setups in Tables 5–9 is not specified. This discrepancy makes the main results irreproducible as reported and needs to be resolved with a clear definition of which experimental configuration generated each table.
  5. [§5.1.2–§5.1.3] The 'good'/'bad' split used for the FID analysis and for the downstream comparison in Table 4 is based on an ad hoc procedure: initially 5,000 'good' and 'bad' images in total were manually selected, then a binary classifier was trained to label the remaining images. No details are given for this classifier (architecture, training set size per class, accuracy, or whether it was trained on the same classes), and it is unclear how many images of each type were used in the downstream pretraining of Table 4. Without these details, the FID scores in Table 3 and the comparison in Table 4 cannot be interpreted quantitatively, and the claim that both 'good' and 'bad' images contribute positively is not properly supported.
minor comments (6)
  1. [§3.4] The text states that the augmented dataset contains 12,700 images, but Table 1 lists a total of 12,400 images in FETAL_PLANES_DB; this inconsistency should be corrected.
  2. [§1 and §6] The paper repeatedly refers to 'six different classifiers' (in the abstract contributions and conclusion), but only five classifiers (ResNet50, DenseNet169, ViT_b_32, Swin_t, MedMamba) are used in the main experiments; the sixth appears only in the ensemble vote in Table 11, and the text should be clarified.
  3. [§2.2] There are typographical issues such as 'V AEs' (should be 'VAEs') and inconsistent spacing in the related-work section; a careful proofread is needed.
  4. [§3.2 and §4.4] The 'half U-Net' used as the classifier for classifier guidance is described only briefly; the architecture, number of parameters, and training details (including how the gradient is injected during sampling) are not specified, which prevents replication of the generation pipeline.
  5. [§5.2.5] The comparisons to Krishna and Kokil [2024a] and Krishna and Kokil [2023] report lower accuracies (94.64% and 93.86%) than the original papers (95.69% and 95.5%), but it is not stated whether the same train/test split, image size, and checkpoint selection were used; this makes the comparison difficult to assess.
  6. [Availability] The paper states that the synthetic images 'will be made publicly available' but provides no link or code release; given that the main contribution is the dataset, the authors should release the images, the generation code, and the training scripts at publication time.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper's claim is an empirical comparison, and no load-bearing step reduces to its own inputs.

full rationale

This paper makes an empirical claim—pretraining classifiers on diffusion-generated synthetic fetal ultrasound images and then fine-tuning on real images yields higher accuracy—supported by experimental tables rather than by a derivation. There is no formal derivation chain whose outputs are defined in terms of their inputs. The diffusion model is trained on the same real training split later used for fine-tuning, but this is a standard data-reuse design, not a case of a predicted quantity being a fitted parameter renamed as a finding. The 'good'/'bad' image selection in Section 5.1.2 is post hoc, but both categories are used for downstream pretraining, and the binary quality classifier is not presented as predicting downstream accuracy. No load-bearing self-citation appears: references to Dhariwal and Nichol and other methods are external prior work with independent content. The main weakness—that the headline comparison changes dataset size, training budget, and data source simultaneously, and that the Table 12 ablation partly contradicts the diffusion-specific attribution—is a threat to the causal interpretation of the experiments, not a circularity. Thus none of the enumerated circularity patterns is present.

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

No new physical or theoretical entities are introduced. The paper's assumptions are standard machine learning background and dataset assumptions. The most consequential assumption is that the accuracy gain is due to the synthetic data distribution and not the larger training budget.

assumptions (4)
  • standard math The diffusion model training objective (L_simple = E ||epsilon - epsilon_theta||^2) is a valid and standard way to train a denoising diffusion model.
    Invoked in Section 3.1, based on Dhariwal and Nichol 2021 and Ho et al. 2020; the paper relies on this as background.
  • domain assumption The class labels in FETAL_PLANES_DB are correct and the six-plane categorization is a meaningful classification target.
    Section 3.3 treats this public dataset as ground truth; if labels are noisy, the measured accuracy and the synthetic class conditioning are both affected.
  • domain assumption The synthetic images generated by the diffusion model do not contain memorized copies of test images.
    Section 5.1 only provides visual inspection and FID, not a near-duplicate analysis; if the generator leaked test images, the downstream accuracy gains would be inflated.
  • domain assumption The observed accuracy gain from the two-stage pipeline is caused by the synthetic images rather than by the larger number of training iterations.
    Tables 5-9 compare a 200-epoch baseline on 9,923 real images with a 200-epoch pretraining on 30,000 or 60,000 synthetic images followed by fine-tuning; the training budgets are not matched.

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Cite this review

Pith. "Pith review of Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models." pith.science (2026). https://pith.science/paper/ACILB7R2

@misc{pith2026250115248,
  author       = {Pith},
  title        = {Pith review of: Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ACILB7R2}},
  note         = {Machine review of arXiv:2501.15248}
}
read the original abstract

Ultrasound imaging is widely used in medical diagnosis, especially for fetal health assessment. However, the availability of high-quality annotated ultrasound images is limited, which restricts the training of machine learning models. In this paper, we investigate the use of diffusion models to generate synthetic ultrasound images to improve the performance on fetal plane classification. We train different classifiers first on synthetic images and then fine-tune them with real images. Extensive experimental results demonstrate that incorporating generated images into training pipelines leads to better classification accuracy than training with real images alone. The findings suggest that generating synthetic data using diffusion models can be a valuable tool in overcoming the challenges of data scarcity in ultrasound medical imaging.

Figures

Figures reproduced from arXiv: 2501.15248 by the authors.

Figure 1
Figure 1. Examples of real images in the FETAL_PLANES_DB dataset. [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Examples of synthetic images generated by the trained diffusion model. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Confusion matrices using Vit_b_32. 11 [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Confusion matrices using Swin_t. 5.2.4 Majority Vote To further enhance the robustness and accuracy of our fetal plane classification, we employed an ensemble approach. Specifically, for each of the base classifier models (ViT_b_32, Swin_t, ResNet50, DenseNet169, and M…

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Reference graph

Works this paper leans on

36 extracted references · 27 canonical work pages · cited by 1 Pith paper

  1. [1]

    Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the lidc-idri database: a systematic review

    Lea Marie Pehrson, Michael Bachmann Nielsen, and Carsten Ammitzb l Lauridsen. Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the lidc-idri database: a systematic review. Diagnostics, 9 0 (1): 0 29, 2019

    work page 2019

  2. [2]

    Medical image segmentation using deep learning: A survey

    Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang, Hongying Meng, and Asoke K Nandi. Medical image segmentation using deep learning: A survey. IET image processing, 16 0 (5): 0 1243--1267, 2022

    work page 2022

  3. [3]

    A review of the application of deep learning in medical image classification and segmentation

    Lei Cai, Jingyang Gao, and Di Zhao. A review of the application of deep learning in medical image classification and segmentation. Annals of translational medicine, 8 0 (11), 2020

    work page 2020

  4. [4]

    Handwritten digit recognition with a back-propagation network

    Yann LeCun, Bernhard Boser, John Denker, Donnie Henderson, Richard Howard, Wayne Hubbard, and Lawrence Jackel. Handwritten digit recognition with a back-propagation network. Advances in neural information processing systems, 2, 1989

    1989

  5. [5]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770--778, 2016

    2016

  6. [6]

    Novel transfer learning approach for medical imaging with limited labeled data

    Laith Alzubaidi, Muthana Al-Amidie, Ahmed Al-Asadi, Amjad J Humaidi, Omran Al-Shamma, Mohammed A Fadhel, Jinglan Zhang, Jesus Santamar \' a, and Ye Duan. Novel transfer learning approach for medical imaging with limited labeled data. Cancers, 13 0 (7): 0 1590, 2021

    work page 2021

  7. [7]

    Dense prediction and local fusion of superpixels: A framework for breast anatomy segmentation in ultrasound image with scarce data

    Qinghua Huang, Zhaoji Miao, Shichong Zhou, Cai Chang, and Xuelong Li. Dense prediction and local fusion of superpixels: A framework for breast anatomy segmentation in ultrasound image with scarce data. IEEE Transactions on Instrumentation and Measurement, 70: 0 1--8, 2021

    work page 2021

  8. [8]

    Machine learning for medical ultrasound: status, methods, and future opportunities

    Laura J Brattain, Brian A Telfer, Manish Dhyani, Joseph R Grajo, and Anthony E Samir. Machine learning for medical ultrasound: status, methods, and future opportunities. Abdominal radiology, 43: 0 786--799, 2018

    work page 2018

Show all 36 references
  1. [9]

    u tte, J \

    August DuMont Sch \"u tte, J \"u rgen Hetzel, Sergios Gatidis, Tobias Hepp, Benedikt Dietz, Stefan Bauer, and Patrick Schwab. Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation. NPJ digital medicine, 4 0 (1): 0 141, 2021

    2021

  2. [10]

    Generative adversarial nets

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014

    2014

  3. [11]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33: 0 6840--6851, 2020

    2020

  4. [12]

    Gan-based synthetic medical image augmentation for increased cnn performance in liver lesion classification

    Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger, and Hayit Greenspan. Gan-based synthetic medical image augmentation for increased cnn performance in liver lesion classification. Neurocomputing, 321: 0 321--331, 2018

    2018

  5. [13]

    Data augmentation using generative adversarial networks (gans) for gan-based detection of pneumonia and covid-19 in chest x-ray images

    Saman Motamed, Patrik Rogalla, and Farzad Khalvati. Data augmentation using generative adversarial networks (gans) for gan-based detection of pneumonia and covid-19 in chest x-ray images. Informatics in medicine unlocked, 27: 0 100779, 2021

    2021

  6. [14]

    Generative adversarial network in medical imaging: A review

    Xin Yi, Ekta Walia, and Paul Babyn. Generative adversarial network in medical imaging: A review. Medical image analysis, 58: 0 101552, 2019

    2019

  7. [15]

    2d medical image synthesis using transformer-based denoising diffusion probabilistic model

    Shaoyan Pan, Tonghe Wang, Richard LJ Qiu, Marian Axente, Chih-Wei Chang, Junbo Peng, Ashish B Patel, Joseph Shelton, Sagar A Patel, Justin Roper, et al. 2d medical image synthesis using transformer-based denoising diffusion probabilistic model. Physics in Medicine & Biology, 6...

    2023

  8. [16]

    Improving nonalcoholic fatty liver disease classification performance with latent diffusion models

    Romain Hardy, Joe Klepich, Ryan Mitchell, Steve Hall, Jericho Villareal, and Cornelia Ilin. Improving nonalcoholic fatty liver disease classification performance with latent diffusion models. Scientific Reports, 13 0 (1): 0 21619, 2023

    2023

  9. [17]

    Noise-consistent siamese-diffusion for medical image synthesis and segmentation

    Kunpeng Qiu, Zhiqiang Gao, Zhiying Zhou, Mingjie Sun, and Yongxin Guo. Noise-consistent siamese-diffusion for medical image synthesis and segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 15672--15681, 2025

    2025

  10. [18]

    On convergence and stability of gans

    Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira. On convergence and stability of gans. arXiv preprint arXiv:1705.07215, 2017

    2017 arXiv

  11. [19]

    Diffusion models beat gans on image synthesis

    Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34: 0 8780--8794, 2021

    2021

  12. [20]

    Differential data augmentation techniques for medical imaging classification tasks

    Zeshan Hussain, Francisco Gimenez, Darvin Yi, and Daniel Rubin. Differential data augmentation techniques for medical imaging classification tasks. In AMIA annual symposium proceedings, volume 2017, page 979. American Medical Informatics Association, 2017

    2017

  13. [21]

    Medical image data augmentation: techniques, comparisons and interpretations

    Evgin Goceri. Medical image data augmentation: techniques, comparisons and interpretations. Artificial Intelligence Review, 56 0 (11): 0 12561--12605, 2023

    2023

  14. [22]

    Data augmentation and transfer learning to improve generalizability of an automated prostate segmentation model

    Thomas H Sanford, Ling Zhang, Stephanie A Harmon, Jonathan Sackett, Dong Yang, Holger Roth, Ziyue Xu, Deepak Kesani, Sherif Mehralivand, Ronaldo H Baroni, et al. Data augmentation and transfer learning to improve generalizability of an automated prostate segmentation model. Am...

    2020

  15. [23]

    Generative adversarial networks to improve fetal brain fine-grained plane classification

    Alberto Montero, Elisenda Bonet-Carne, and Xavier Paolo Burgos-Artizzu. Generative adversarial networks to improve fetal brain fine-grained plane classification. Sensors, 21 0 (23): 0 7975, 2021

    2021

  16. [24]

    Unified brain mr-ultrasound synthesis using multi-modal hierarchical representations

    Reuben Dorent, Nazim Haouchine, Fryderyk Kogl, Samuel Joutard, Parikshit Juvekar, Erickson Torio, Alexandra J Golby, Sebastien Ourselin, Sarah Frisken, Tom Vercauteren, et al. Unified brain mr-ultrasound synthesis using multi-modal hierarchical representations. In Internationa...

    2023

  17. [25]

    Automated classification of common maternal fetal ultrasound planes using multi-layer perceptron with deep feature integration

    Thunakala Bala Krishna and Priyanka Kokil. Automated classification of common maternal fetal ultrasound planes using multi-layer perceptron with deep feature integration. Biomedical Signal Processing and Control, 86: 0 105283, 2023

    2023

  18. [26]

    Standard fetal ultrasound plane classification based on stacked ensemble of deep learning models

    Thunakala Bala Krishna and Priyanka Kokil. Standard fetal ultrasound plane classification based on stacked ensemble of deep learning models. Expert Systems with Applications, 238: 0 122153, 2024 a

    2024

  19. [27]

    Automated deep bottleneck residual 82-layered architecture with bayesian optimization for the classification of brain and common maternal fetal ultrasound planes

    Fatima Rauf, Muhammad Attique Khan, Ali Kashif Bashir, Kiran Jabeen, Ameer Hamza, Ahmed Ibrahim Alzahrani, Nasser Alalwan, and Anum Masood. Automated deep bottleneck residual 82-layered architecture with bayesian optimization for the classification of brain and common maternal...

    2023

  20. [28]

    Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers

    Haifa Ghabri, Mohammed S Alqahtani, Soufiene Ben Othman, Amal Al-Rasheed, Mohamed Abbas, Hassan Ali Almubarak, Hedi Sakli, and Mohamed Naceur Abdelkarim. Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare ...

    2023

  21. [29]

    Automatic identification of fetal biometry planes from ultrasound images: An assistive tool for healthcare professionals

    Thunakala Bala Krishna and Priyanka Kokil. Automatic identification of fetal biometry planes from ultrasound images: An assistive tool for healthcare professionals. IEEE Sensors Journal, 2024 b

    2024

  22. [30]

    A deep convolutional neural network with adaptive channel weight technique for automated identification of standard fetal biometry planes

    Thunakala Bala Krishna and Priyanka Kokil. A deep convolutional neural network with adaptive channel weight technique for automated identification of standard fetal biometry planes. IEEE Transactions on Instrumentation and Measurement, 2024 c

    2024

  23. [31]

    Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

    Xavier P Burgos-Artizzu, David Coronado-Guti \'e rrez, Brenda Valenzuela-Alcaraz, Elisenda Bonet-Carne, Elisenda Eixarch, Fatima Crispi, and Eduard Gratac \'o s. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound p...

    2020

  24. [32]

    Densely connected convolutional networks

    Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700--4708, 2017

    2017

  25. [33]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020

    2010 arXiv

  26. [34]

    Swin transformer: Hierarchical vision transformer using shifted windows

    Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF international conference on computer vision, pages 10012--10022, 2021

    2021

  27. [35]

    Medmamba: Vision mamba for medical image classification

    Yubiao Yue and Zhenzhang Li. Medmamba: Vision mamba for medical image classification. arXiv preprint arXiv:2403.03849, 2024

    2024 arXiv

  28. [36]

    Adam: A method for stochastic optimization

    Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014

    2014 arXiv

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