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BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read BS-LDM, a conditional latent diffusion model, generates soft-tissue chest X-rays with a bone-suppression ratio of 0.976 on the new SZCH-X-Rays dataset, surpassing seven prior methods.

desk verdict The SZCH-X-Rays result looks like a genuine step forward for bone suppression, but the JSRT half of the comparison rests on soft-tissue ground truth that the cited JSRT database does not contain. read the letter →

arxiv 2412.15670 v5 pith:K2ZMCTX2 submitted 2024-12-20 eess.IV cs.CV

Yifei Sun , Zhanghao Chen , Hao Zheng , Wenming Deng , Jin Liu , Wenwen Min , Ahmed Elazab , Xiang Wan
show 2 more authors
Changmiao Wang Ruiquan Ge
This is my paper · ORCID
classification eess.IVcs.CV
keywords bonesuppressionchestX-raylatentdiffusionmodelsdual-energysubtractionsofttissueimagingVQGANoffsetnoisetemporaladaptivethresholding
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 claims that a conditional latent diffusion model, BS-LDM, can turn a single high-resolution chest X-ray into a soft-tissue image with bones largely removed, approaching the output of dual-energy subtraction without requiring specialized two-exposure equipment. On the authors' new 818-pair hospital dataset, SZCH-X-Rays, and on the public JSRT dataset, BS-LDM outperforms seven existing bone-suppression methods on all four reported metrics. Clinical and automated downstream evaluations indicate that the generated soft-tissue images improve detection of lung lesions compared with the original X-rays. If the claim holds, bone suppression becomes practical for routine single-exposure chest X-rays at a lower computational cost than earlier diffusion-based approaches.

What carries the argument

The central object is ML-VQGAN, a vector-quantized generative adversarial network constrained by a multi-level hybrid loss that combines L1, perceptual, adversarial, and quantization terms to build a perceptually faithful latent space. It carries the argument by compressing high-resolution chest X-rays into a low-dimensional latent manifold where conditional latent diffusion becomes computationally feasible while preserving texture detail. The second mechanism is offset noise, which augments Gaussian noise with zero-frequency bias to compensate for the greater resistance of low-frequency image content to standard noise injection. The third is temporal adaptive thresholding, which clips the latent variable during each reverse sampling step using a threshold $s=\omega t+b$ that expands over time, preventing pixel saturation while allowing contrast to match real soft-tissue images.

What would settle it

Run BS-LDM on paired CXR/DES images collected from a different scanner, hospital, or exposure setting and compare BSR and LPIPS against the same-scanner test results; a large drop would show the learned mapping is tied to one acquisition setup rather than a general bone-suppression solution.

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Extended reading notes

Core claim

The central claim is that BS-LDM, an end-to-end framework built on a conditional latent diffusion model, achieves state-of-the-art bone suppression in high-resolution chest X-rays. The model compresses each 1024x1024 image into a 4x128x128 latent space using a vector-quantized GAN trained with a multi-level hybrid reconstruction loss, then runs a diffusion denoising process conditioned on the CXR latent by channel concatenation. Two additions target the low-frequency errors typical of diffusion models: offset noise in the forward process injects zero-frequency bias to correct luminance drift, and a temporal adaptive thresholding strategy clips latent pixels with a threshold that grows linearly with the sampling timestep. On SZCH-X-Rays the method reports a bone suppression ratio of 0.976, MSE of 0.00060, PSNR of 33.224 dB, and LPIPS of 0.051; on JSRT it reports 0.922, 0.00071, 34.312 dB, and 0.049, all better than the compared baselines. Ablation studies show that removing either offset noise or temporal adaptive thresholding substantially degrades low-frequency fidelity and pixel-intensity alignment.

Load-bearing premise

The load-bearing premise is that dual-energy-subtraction soft-tissue images from one GE scanner, together with inverted and contrast-adjusted JSRT images, define what correct bone-suppressed output looks like for all chest X-rays.

Editorial extensions

If this is right

  • If BS-LDM performs as reported, single-exposure chest X-rays can receive bone suppression close to dual-energy subtraction quality without needing specialized DES hardware or extra radiation.
  • Radiologists reading the generated soft-tissue images would be expected to detect more lung lesions: the paper reports junior radiologist F1 rising from 0.51 to 0.63 and senior F1 from 0.60 to 0.75.
  • Automated classifiers trained on chest X-rays would improve when fed BS-LDM soft-tissue images, with sensitivity gains of about 13.5%, 3.24%, and 3.18% for AlexNet, DenseNet, and ResNet on the Shenzhen dataset.
  • Because BS-LDM's inference time is about 77.7% of BS-Diff and DDPM, high-resolution diffusion-based bone suppression is more practical in clinical settings than prior diffusion baselines.

Reading between the lines

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

  • The paper does not test cross-scanner generalization for the generation task, so a natural extension is to evaluate BS-LDM on paired CXR/DES data from other vendors, exposure settings, or post-processing pipelines; the current ground truth comes from a single GE Discovery XR656 unit.
  • The offset-noise and temporal-adaptive-thresholding fixes are specific to low-frequency drift, which suggests they could transfer to other medical image translation tasks with similar brightness or contrast instability, though the paper only demonstrates them for bone suppression.
  • The construction of the SZCH-X-Rays dataset and the reprocessed JSRT negative-image pairs may serve as a benchmark for future bone-suppression work, but the JSRT processing steps are not independently validated against clinically acquired dual-energy images.
  • The claim that BS-LDM preserves lesions relies on radiologist review and automated classifiers rather than direct registration of lesions between CXR and generated tissue images; a pixel-level lesion-preservation analysis would strengthen the clinical conclusion.
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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

4 major / 4 minor

Summary. The manuscript presents BS-LDM, a conditional latent diffusion model for bone suppression in high-resolution chest X-rays. The method compresses images with a custom VQGAN (ML-VQGAN), trains the diffusion model conditioned on CXR via channel concatenation, adds offset noise to the forward process, and applies a time-dependent clipping threshold during sampling. The authors introduce a new paired CXR/DES dataset (SZCH-X-Rays, 818 pairs), compare against seven prior methods on SZCH-X-Rays and JSRT, and report radiologist and automated downstream evaluations on Shenzhen chest X-rays. The central claim is that BS-LDM outperforms state-of-the-art bone suppression methods on both SZCH-X-Rays and JSRT while improving downstream lung-disease detection.

Significance. If the results are reproducible, this would be a strong practical contribution and likely the first latent-diffusion approach for this task. The paper includes broad comparisons, a clinical reader study, and an external downstream evaluation, and the authors state that code is available. However, the JSRT half of the comparison rests on a data provenance statement that is contradicted by the cited public database, and the reported numbers for hyperparameter sweeps are selected on the same test sets used for the headline comparison. Both points must be resolved before the performance claims can be accepted.

major comments (4)
  1. [Section III-A, Table I] The paper states that "we processed 241 pairs of CXR and DES images from the JSRT dataset, the largest open-source collection available" and uses these pairs for the JSRT rows of Table I. The public JSRT database cited as [10] contains 247 chest radiographs, with and without nodules, and does not contain dual-energy subtraction soft-tissue images; the operations described (inversion and contrast adjustment) cannot produce a soft-tissue ground truth. The BSR metric in Eq. (12) also requires a bone image B, which is not part of JSRT. The JSRT results in Table I, Table II, Fig. 5, and Fig. 8(b) are therefore based on unverifiable or non-existent ground truth. The authors must either provide a verifiable source for these 241 DES pairs, with documentation and release, or remove the JSRT-based comparisons and revise the abstract, introduction, and conclusions accordingly.
  2. [Section III-F, Fig. 8, Table I] The hyperparameters lambda (offset noise weight), omega and b (temporal threshold slope/intercept), and the ML-VQGAN loss weights in Tables III-IV were selected by sweeps on SZCH-X-Rays and JSRT, and the same datasets are then used to report the headline results in Table I. This is a selection-on-test-set procedure: the reported improvements may reflect tuning to the evaluation data rather than a model property. The authors should use the 8:1:1 validation split for hyperparameter selection and report results for the chosen configuration on the untouched test split.
  3. [Section III-A, Section III-G] Thirteen image pairs with severe motion artifacts, pleural effusions, and pneumothorax were excluded from SZCH-X-Rays, and the diagnostic utility assessment uses only 79 lesion-containing pairs. These exclusions remove exactly the clinically challenging cases where bone suppression would matter most, yet the abstract and conclusion state that the results "underscore its clinical value" without this qualification. The authors should report performance on the excluded cases or explicitly restrict the clinical claim to the studied population.
  4. [Table I, Section III-D] Across many comparisons in Table I, the reported standard deviations overlap substantially (e.g., BSR 0.976 +/- 0.018 vs. 0.961 +/- 0.022; PSNR 33.224 +/- 3.577 vs. 32.181 +/- 3.296 for BS-Diff), but no significance tests or confidence intervals are provided. The claim of consistent superiority over all baselines would be strengthened by paired significance testing or effect sizes on the matched image pairs.
minor comments (4)
  1. [Section II-D] There is a typo: "Nervertheless" should be "Nevertheless".
  2. [Fig. 8(b)] The axis labeling in Fig. 8(b) is confusing: the text reports b = 1.4, while the axis appears to be labeled with a "b (x 10^-3)" tick pattern; the units and the roles of omega and b should be clarified.
  3. [Section III-B, code availability] The SZCH-X-Rays dataset is not made publicly available despite the abstract emphasizing its compilation; code availability alone is insufficient for reproducing Table I on that dataset.
  4. [Section III-G] The clinical evaluation does not state whether the radiologists were blinded to the CXR versus generated soft-tissue condition, and the selection process for the 79 abnormal cases is not described; these details should be added.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: BS-LDM is trained and evaluated against held-out DES soft-tissue targets, with baselines reproduced from open-source code; remaining concerns are validity/reproducibility issues, not identity reductions.

full rationale

The paper's claimed derivation is self-contained at the equation level: Eq. (10) defines the offset-noise injection, Eq. (2) trains the estimator to predict exactly that injected noise, and the ML-VQGAN reconstruction losses in Eqs. (4)-(9) target the actual soft-tissue images, not quantities that already contain the model's output. Table I evaluates against held-out DES ground truth from SZCH-X-Rays and reports comparisons to seven baselines, including BS-Diff [24]; BS-Diff is a self-citation, but the paper states it was re-run from open-source code with default parameters, so it functions as independent evidence rather than a load-bearing cited conclusion. The two genuine concerns are evaluation-validity issues, not construction-level circularity: (i) Section III-F and Fig. 8 select lambda, omega, and b by optimizing PSNR on the same datasets used for the final numbers, which inflates results but does not make any metric equal to its input by definition; and (ii) Section III-A's claim of 241 JSRT CXR/DES pairs cannot be verified against the cited JSRT database [10], but the paper never states that those soft-tissue targets were produced by the tested model or by a learned bone-suppression method, so no specific circular reduction can be exhibited. Therefore, no circular step meets the evidentiary bar set by the analysis rules.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claim rests on a learned image translation model and two ground-truth assumptions about DES images. No new physical entities are introduced. The main free parameters are hyperparameters selected via validation and evaluation-set sweeps, which should be weighed when assessing the strength of the reported improvements.

free parameters (4)
  • offset noise weight lambda = 0.1
    Chosen by hyperparameter analysis on SZCH-X-Rays and JSRT (Fig. 8a); controls low-frequency bias added during forward diffusion and directly affects brightness and contrast.
  • temporal threshold slope omega = 0.003
    Chosen by hyperparameter analysis (Fig. 8b); defines how the clipping range changes with timestep in the reverse process.
  • temporal threshold intercept b = 1.4
    Chosen by hyperparameter analysis (Fig. 8b); sets the base threshold range for the temporal adaptive thresholding strategy.
  • ML-VQGAN loss weights lambda_L1, lambda_Qua, lambda_Per, lambda_Adv = 1, 1, 0.001, 0.01
    Selected via weight sweeps in Tables III and IV; balance reconstruction, quantization, perceptual, and adversarial losses and affect the latent space used by the diffusion model.
assumptions (6)
  • standard math The standard DDPM forward and reverse processes (Eqs. 1, 3) are correct and applicable in the VQGAN latent space.
    The method inherits the diffusion mathematics from Ho et al. [21] and Rombach et al. [25] without re-deriving it.
  • domain assumption DES soft tissue images from a single GE scanner at one hospital are accurate ground truth for bone suppression.
    Section III-A uses 818 paired CXR and DES images as supervision; any misregistration, motion artifact, or scanner-specific bias is passed to the model and to the evaluation.
  • domain assumption Inverted and contrast-adjusted JSRT images are valid soft tissue ground truth.
    Section III-A converts 241 JSRT pairs into negative images; no validation is provided that this conversion preserves the true soft tissue appearance.
  • domain assumption The ML-VQGAN latent space preserves clinically relevant details, including small lesions.
    Section II-C compresses 1024x1024 images to a 128x128 latent space; the paper does not measure whether perceptual compression discards small nodules.
  • domain assumption VGG16 features pretrained on ImageNet are an appropriate perceptual loss for medical chest X-ray images.
    Section II-C1 uses VGG16 for perceptual loss without medical-domain adaptation.
  • ad hoc to paper The linear temporal threshold s = omega*t + b with omega > 0 and b >= 1 is a valid and sufficient model for contrast correction during sampling.
    Section II-E introduces this functional form without derivation; its parameters are fitted to the evaluation datasets.

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Cite this review

Pith. "Pith review of BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models." pith.science (2026). https://pith.science/paper/K2ZMCTX2

@misc{pith2026241215670,
  author       = {Pith},
  title        = {Pith review of: BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K2ZMCTX2}},
  note         = {Machine review of arXiv:2412.15670}
}
read the original abstract

Lung diseases represent a significant global health challenge, with Chest X-Ray (CXR) being a key diagnostic tool due to its accessibility and affordability. Nonetheless, the detection of pulmonary lesions is often hindered by overlapping bone structures in CXR images, leading to potential misdiagnoses. To address this issue, we develop an end-to-end framework called BS-LDM, designed to effectively suppress bone in high-resolution CXR images. This framework is based on conditional latent diffusion models and incorporates a multi-level hybrid loss-constrained vector-quantized generative adversarial network which is crafted for perceptual compression, ensuring the preservation of details. To further enhance the framework's performance, we utilize offset noise in the forward process, and a temporal adaptive thresholding strategy in the reverse process. These additions help minimize discrepancies in generating low-frequency information of soft tissue images. Additionally, we have compiled a high-quality bone suppression dataset named SZCH-X-Rays. This dataset includes 818 pairs of high-resolution CXR and soft tissue images collected from our partner hospital. Moreover, we processed 241 data pairs from the JSRT dataset into negative images, which are more commonly used in clinical practice. Our comprehensive experiments and downstream evaluations reveal that BS-LDM excels in bone suppression, underscoring its clinical value. Our code is available at https://github.com/diaoquesang/BS-LDM.

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Forward citations

Cited by 1 Pith paper

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  1. GL-LCM: Global-Local Latent Consistency Models for Fast High-Resolution Bone Suppression in Chest X-Ray Images

    eess.IV 2025-08 conditional novelty 5.0 of 10

    A global-local latent consistency model with local-enhanced guidance performs fast bone suppression in chest X-rays, running in 8.5 seconds per image while improving quality metrics over diffusion baselines.

Reference graph

Works this paper leans on

47 extracted references · 32 canonical work pages · cited by 1 Pith paper

  1. [10]

    Shiraishi, S

    J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi, K.- i. Komatsu, M. Matsui, H. Fujita, Y . Kodera, and K. Doi, “Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules,”American Journal of Roentgenology, vol....

    work page 2000

  2. [1]

    Cnn-moe based framework for classification of respiratory anomalies and lung disease detection,

    L. Pham, H. Phan, R. Palaniappan, A. Mertins, and I. McLoughlin, “Cnn-moe based framework for classification of respiratory anomalies and lung disease detection,”IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 8, pp. 2938–2947, 2021

    work page 2021

  3. [2]

    Epidemiology and management of paediatric pulmonary embolism: a much-needed focus of research,

    K. Zhen, P. Yang, and Z. Zhai, “Epidemiology and management of paediatric pulmonary embolism: a much-needed focus of research,”The Lancet Respiratory Medicine, 2025

    work page 2025

  4. [3]

    Missed bronchogenic carci- noma: radiographic findings in 27 patients with a potentially resectable lesion evident in retrospect

    J. Austin, B. Romney, and L. Goldsmith, “Missed bronchogenic carci- noma: radiographic findings in 27 patients with a potentially resectable lesion evident in retrospect.”Radiology, vol. 182, no. 1, pp. 115–122, 1992

    work page 1992

  5. [4]

    High- resolution chest x-ray bone suppression using unpaired ct structural priors,

    H. Li, H. Han, Z. Li, L. Wang, Z. Wu, J. Lu, and S. K. Zhou, “High- resolution chest x-ray bone suppression using unpaired ct structural priors,”IEEE Transactions on Medical Imaging, vol. 39, no. 10, pp. 3053–3063, 2020

    work page 2020

  6. [5]

    Rib suppression-based radiomics for diagnosis of neonatal respiratory distress syndrome in chest x-rays,

    Y . Wang, Z. Qu, Z. Dai, Y . Li, Y . Liu, W. Wang, L. Xiao, and Y . Zhang, “Rib suppression-based radiomics for diagnosis of neonatal respiratory distress syndrome in chest x-rays,”Scientific Reports, vol. 15, no. 1, p. 4416, 2025

    work page 2025

  7. [6]

    Dual energy subtraction: principles and clinical applications,

    P. V ock and Z. Szucs-Farkas, “Dual energy subtraction: principles and clinical applications,”European Journal of Radiology, vol. 72, no. 2, pp. 231–237, 2009

    work page 2009

  8. [7]

    Image-processing technique for suppressing ribs in chest radiographs by means of mas- sive training artificial neural network (mtann),

    K. Suzuki, H. Abe, H. MacMahon, and K. Doi, “Image-processing technique for suppressing ribs in chest radiographs by means of mas- sive training artificial neural network (mtann),”IEEE Transactions on Medical Imaging, vol. 25, no. 4, pp. 406–416, 2006

    work page 2006

Show all 47 references
  1. [8]

    Elimination of clavicle shadows to help automatic lung nodule detection on chest radiographs,

    G. Simk ´o, G. Orb´an, P. M´aday, and G. Horv´ath, “Elimination of clavicle shadows to help automatic lung nodule detection on chest radiographs,” in4th European Conference of the International Federation for Medical and Biological Engineering: ECIFMBE 2008 23–27 November 2008...

    2008

  2. [9]

    Segmentation of anatomical structures on chest radiographs,

    S. Juh ´asz, ´A. Horv ´ath, L. Nikh ´azy, G. Horv ´ath, and ´A. Horv ´ath, “Segmentation of anatomical structures on chest radiographs,” inXII Mediterranean Conference on Medical and Biological Engineering and Computing 2010: May 27–30, 2010 Chalkidiki, Greece. Springer, 2010,...

    2010

  3. [11]

    Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain,

    W. Yang, Y . Chen, Y . Liu, L. Zhong, G. Qin, Z. Lu, Q. Feng, and W. Chen, “Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain,”Medical Image Analysis, vol. 35, pp. 421–433, 2017

    2017

  4. [12]

    Deep learning models for bone suppression in chest radiographs,

    M. Gusarev, R. Kuleev, A. Khan, A. R. Rivera, and A. M. Khattak, “Deep learning models for bone suppression in chest radiographs,” in 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2017, pp. 1–7

    2017

  5. [13]

    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,” Advances in Neural Information Processing Systems, vol. 27, 2014

    2014

  6. [14]

    Generation of virtual dual energy images from standard single-shot radiographs using multi- scale and conditional adversarial network,

    B. Zhou, X. Lin, B. Eck, J. Hou, and D. Wilson, “Generation of virtual dual energy images from standard single-shot radiographs using multi- scale and conditional adversarial network,” inComputer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, Dec...

    2018

  7. [15]

    Bone suppression of chest radiographs with cascaded convolutional networks in wavelet domain,

    Y . Chen, X. Gou, X. Feng, Y . Liu, G. Qin, Q. Feng, W. Yang, and W. Chen, “Bone suppression of chest radiographs with cascaded convolutional networks in wavelet domain,”IEEE Access, vol. 7, pp. 8346–8357, 2019

    2019

  8. [16]

    Chest x-ray bone suppression for improving classification of tuberculosis- consistent findings,

    S. Rajaraman, G. Zamzmi, L. Folio, P. Alderson, and S. Antani, “Chest x-ray bone suppression for improving classification of tuberculosis- consistent findings,”Diagnostics, vol. 11, no. 5, p. 840, 2021

    2021

  9. [17]

    Bone suppression of lateral chest x-rays with imperfect and limited dual-energy subtraction images,

    Y . Liu, F. Zeng, M. Ma, B. Zheng, Z. Yun, G. Qin, W. Yang, and Q. Feng, “Bone suppression of lateral chest x-rays with imperfect and limited dual-energy subtraction images,”Computerized Medical Imaging and Graphics, vol. 105, p. 102186, 2023

    2023

  10. [18]

    A multi-modal bone suppression, lung segmentation, and classification approach for accurate covid-19 detection using chest radiographs,

    G. Rani, A. Misra, V . S. Dhaka, D. Buddhi, R. K. Sharma, E. Zumpano, and E. V ocaturo, “A multi-modal bone suppression, lung segmentation, and classification approach for accurate covid-19 detection using chest radiographs,”Intelligent Systems with Applications, vol. 16, p. 2...

    2022

  11. [19]

    Mda- gan: Multi-scale and dual attention generative adversarial network for bone suppression in chest x-rays,

    A. Singh, R. Hussain, R. Bhattacharya, B. Lall, B. Panigrahi, A. Agrawal, A. Agrawal, B. Thangakunam, and D. Christopher, “Mda- gan: Multi-scale and dual attention generative adversarial network for bone suppression in chest x-rays,”IEEE Transactions on Artificial Intelligence, 2024

    2024

  12. [20]

    xu- netfullsharp: The novel deep learning architecture for chest x-ray bone shadow suppression,

    V . Schiller, R. Burget, S. Genzor, J. Mizera, and A. Mezina, “xu- netfullsharp: The novel deep learning architecture for chest x-ray bone shadow suppression,”Biomedical Signal Processing and Control, vol. 100, p. 106983, 2025

    2025

  13. [21]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems, vol. 33, pp. 6840– 6851, 2020

    2020

  14. [22]

    Cascaded latent diffusion models for high-resolution chest x-ray synthesis,

    T. Weber, M. Ingrisch, B. Bischl, and D. R ¨ugamer, “Cascaded latent diffusion models for high-resolution chest x-ray synthesis,” inPacific- 12 IEEE TRANSACTIONS AND JOURNALS TEMPLA TE Asia Conference on Knowledge Discovery and Data Mining. Springer, 2023, pp. 180–191

    2023

  15. [23]

    Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis,

    O. A. M. F. Alnaggar, B. N. Jagadale, M. A. N. Saif, O. A. Ghaleb, A. A. Ahmed, H. A. A. Aqlan, and H. D. E. Al-Ariki, “Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis,”Artificial Intelligenc...

    2024

  16. [24]

    Bs-diff: Effective bone suppression using conditional diffusion models from chest x-ray images,

    Z. Chen, Y . Sun, R. Ge, W. Qin, C. Pan, W. Deng, Z. Liu, W. Min, A. Elazab, X. Wanet al., “Bs-diff: Effective bone suppression using conditional diffusion models from chest x-ray images,” in2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024, pp. 1–5

    2024

  17. [25]

    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 Recogni- tion, 2022, pp. 10 684–10 695

    2022

  18. [26]

    Diffusion with offset noise,

    N. Guttenberg, “Diffusion with offset noise,” https://www.crosslabs.org/ blog/diffusion-with-offset-noise, 2023

    2023

  19. [27]

    Align your latents: High-resolution video synthesis with latent diffusion models,

    A. Blattmann, R. Rombach, H. Ling, T. Dockhorn, S. W. Kim, S. Fidler, and K. Kreis, “Align your latents: High-resolution video synthesis with latent diffusion models,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 22 563–22 575

    2023

  20. [28]

    Controlnet++: Improving conditional controls with efficient consistency feedback: Project page: liming-ai. github. io/controlnet plus plus,

    M. Li, T. Yang, H. Kuang, J. Wu, Z. Wang, X. Xiao, and C. Chen, “Controlnet++: Improving conditional controls with efficient consistency feedback: Project page: liming-ai. github. io/controlnet plus plus,” in European Conference on Computer Vision. Springer, 2024, pp. 129– 147

    2024

  21. [29]

    Lpuwf-ldm: Enhanced latent diffusion model for precise late-phase uwf-fa generation on limited dataset,

    Z. Fang, X. Yu, G. Zhou, K. Zhuang, Y . Chen, R. Ge, C. Wang, G. Jia, Q. Wu, J. Yeet al., “Lpuwf-ldm: Enhanced latent diffusion model for precise late-phase uwf-fa generation on limited dataset,”Expert Systems with Applications, vol. 270, p. 126471, 2025

    2025

  22. [30]

    Perceptual losses for real-time style transfer and super-resolution,

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

    2016

  23. [31]

    High-resolution image synthesis and semantic manipulation with con- ditional gans,

    T.-C. Wang, M.-Y . Liu, J.-Y . Zhu, A. Tao, J. Kautz, and B. Catanzaro, “High-resolution image synthesis and semantic manipulation with con- ditional gans,” inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018

    2018

  24. [32]

    Very deep convolutional networks for large-scale image recognition,

    K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,”arXiv preprint arXiv:1409.1556, 2014

    2014 arXiv

  25. [33]

    Classifier-free diffusion guidance,

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

    2022 arXiv

  26. [34]

    Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,

    C. Lu, Y . Zhou, F. Bao, J. Chen, C. Li, and J. Zhu, “Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,”Advances in Neural Information Processing Systems, vol. 35, pp. 5775–5787, 2022

    2022

  27. [35]

    Suppression of translucent elongated structures: applications in chest radiography,

    L. Hogeweg, C. I. S ´anchez, and B. van Ginneken, “Suppression of translucent elongated structures: applications in chest radiography,” IEEE Transactions on Medical Imaging, vol. 32, no. 11, pp. 2099–2113, 2013

    2013

  28. [36]

    Image quality metrics: Psnr vs. ssim,

    A. Hore and D. Ziou, “Image quality metrics: Psnr vs. ssim,” in2010 20th International Conference on Pattern Recognition. IEEE, 2010, pp. 2366–2369

    2010

  29. [37]

    The unreasonable effectiveness of deep features as a perceptual metric,

    R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE Conference On Computer Vision and Pattern Recognition, 2018, pp. 586–595

    2018

  30. [38]

    Imagenet classification with deep convolutional neural networks,

    A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,”Advances in Neural Informa- tion Processing Systems, vol. 25, 2012

    2012

  31. [39]

    Densely connected convolutional networks,

    G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” inProceedings of the IEEE Confer- ence on Computer Vision and Pattern Recognition, 2017, pp. 4700–4708

    2017

  32. [40]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778

    2016

  33. [41]

    Bone suppression on chest radiographs for pulmonary nodule detection: comparison between a generative adversarial network and dual-energy subtraction,

    K. Bae, D. Y . Oh, I. D. Yun, and K. N. Jeon, “Bone suppression on chest radiographs for pulmonary nodule detection: comparison between a generative adversarial network and dual-energy subtraction,”Korean Journal of Radiology, vol. 23, no. 1, p. 139, 2022

    2022

  34. [42]

    Value of bone suppression software in chest radiographs for improving image quality and reducing radiation dose,

    G.-S. Hong, K.-H. Do, A.-Y . Son, K.-W. Jo, K. P. Kim, J. Yun, and C. W. Lee, “Value of bone suppression software in chest radiographs for improving image quality and reducing radiation dose,”European Radiology, vol. 31, pp. 5160–5171, 2021

    2021

  35. [43]

    Interpreting a chest x-ray,

    T. Bansal and R. Beese, “Interpreting a chest x-ray,”British Journal of Hospital Medicine, vol. 80, no. 5, pp. C75–C79, 2019

    2019

  36. [44]

    Two public chest x-ray datasets for computer-aided screen- ing of pulmonary diseases,

    S. Jaeger, S. Candemir, S. Antani, Y .-X. J. W ´ang, P.-X. Lu, and G. Thoma, “Two public chest x-ray datasets for computer-aided screen- ing of pulmonary diseases,”Quantitative Imaging in Medicine and Surgery, vol. 4, no. 6, p. 475, 2014

    2014

  37. [45]

    Latent consistency models: Synthesizing high-resolution images with few-step inference,

    S. Luo, Y . Tan, L. Huang, J. Li, and H. Zhao, “Latent consistency models: Synthesizing high-resolution images with few-step inference,” arXiv preprint arXiv:2310.04378, 2023

    2023 arXiv

  38. [46]

    Scalable diffusion models with transformers,

    W. Peebles and S. Xie, “Scalable diffusion models with transformers,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 4195–4205

    2023

  39. [47]

    Scalable diffusion models with state space backbone,

    Z. Fei, M. Fan, C. Yu, and J. Huang, “Scalable diffusion models with state space backbone,”arXiv preprint arXiv:2402.05608, 2024

    2024 arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.