A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck

Ciro Benito Raggio; Maria Francesca Spadea; Paolo Zaffino

REVIEW 3 major objections 7 minor 33 references

A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck

T0 review · 3 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read Three hospitals train one CBCT-to-CT model without sharing data

desk verdict First FL CBCT-to-sCT feasibility study with a genuinely held-out external test set; the registration confound is acknowledged but under-handled, and missing baselines keep the generalization claim conditional. read the letter →

arxiv 2506.08654 v1 pith:LGQRIA7Y submitted 2025-06-10 physics.med-ph cs.LG

Ciro Benito Raggio , Paolo Zaffino , Maria Francesca Spadea This is my paper

classification physics.med-phcs.LG PACS 87.57.Q

keywords federatedlearningCBCT-to-sCTsynthesisheadandneckradiotherapyconditionalGANPix2PixFedAvg-FedProxaggregationcross-institutionalgeneralizationsynthetic

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

This paper sets out to show that federated learning, training a model across hospitals while keeping each site's imaging data local, can produce a CBCT-to-synthetic-CT translator for the head and neck that works across institutions. The authors train a conditional GAN on data from three European centers and evaluate it on a fourth center's 60-patient dataset that never took part in training. The external results (MAE 75.22 ± 11.81 HU, SSIM 0.904 ± 0.034, PSNR 33.52 ± 2.06 dB) fall inside the range of the participating centers, which is what the claim of cross-center generalization rests on. A sympathetic reader would care because radiotherapy dose calculation needs reliable CT-like images, and current deep-learning solutions are usually single-site and cannot pool data under privacy rules.

What carries the argument

The argument is carried by a Pix2Pix-style conditional generative adversarial network: a 2D U-Net generator with eight encoder-decoder blocks and a PatchGAN discriminator, trained with Randomized Multi-2D sampling so each local client sees slices from axial, sagittal, and coronal planes in random order. At inference, the final volume is the median of slice-wise predictions across the three planes. The federation layers on top are server-side FedAvg, which averages client weights in proportion to dataset size, and client-side FedProx, which adds a proximal term (μ = 3) to penalize local drift; a shared preprocessing pipeline, including LUT-based intensity normalization and CT clipping, harmonizes inputs without exchanging images.

What would settle it

Compute landmark-based target registration error on the external and low-performing center's test scans and correlate it with per-case MAE. If the low-performing center's disadvantage disappears when only well-aligned cases are compared, the cross-center generalization claim is partly an artifact of registration; if it persists, the federated model itself is the limiting factor.

Watch

Extended reading notes

Core claim

The central discovery the paper argues for is that a single federated conditional GAN can generalize across CBCT scanners and protocols without any site-specific fine-tuning. The federated model produced synthetic CTs with image-similarity metrics on the never-seen external center (MAE 75.22 ± 11.81 HU, SSIM 0.904 ± 0.034, PSNR 33.52 ± 2.06 dB) comparable to those at participating centers (MAE 64.38–85.90 HU, SSIM 0.882–0.922, PSNR 32.86–34.91 dB). The authors attribute most of the remaining error to residual misregistration between CBCT and CT rather than to the model failing to synthesize anatomy. They interpret this as evidence that privacy-preserving collaborative training is technically feasible for CBCT-to-sCT and that a federated model can be applied to a new institution without retraining.

Load-bearing premise

The evaluation presupposes that residual misalignment between CBCT and ground-truth CT after the added deformable registration is not a systematic, center-specific confound; the paper itself says no objective and independent method is available to quantify registration errors and invokes registration issues to explain the low-performing center's lower scores.

Editorial extensions

If this is right

A hospital can join a collaborative CBCT-to-sCT training effort without uploading patient images, as long as it applies the shared preprocessing locally.
The resulting model can be applied directly to a new center's data, bypassing per-site fine-tuning and the need for center-specific commercial solutions.
Scanner and protocol differences do not by themselves prevent cross-center performance parity, since the external center, which used a different scanner, matched the training centers.
Remaining failures are concentrated in air and dense-bone regions and in misaligned test pairs, so further gains will likely come from targeted training for underrepresented tissues and from better registration evaluation.

Reading between the lines

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

A testable extension the paper does not run: ablate the LUT normalization to see whether the harmonized intensity range, rather than federated averaging itself, is what makes the external center match.
Rotating the excluded center, holding out each participating center in turn, would test whether generalization holds for any new site and not only for the external center's protocol mix.
Because registration error is only visually assessed, an independent landmark-based registration metric could separate synthesis accuracy from misalignment and would change how the low-performing center's scores are interpreted.
Clinical adoption would additionally require dose-distribution validation, such as plan dose comparison, which the authors note is impossible with the imaging-only dataset.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The genuinely new thing here is the application: federated learning for CBCT-to-sCT synthesis in the head and neck, built on the authors' own FedSynthCT-Brain MRI-to-CT pipeline. As far as I know, it's the first FL result for this task, and they use the public SynthRAD2025 data, which makes the work reproducible. The external validation on 60 patients from Center A, completely held out from training, is a real strength — those metrics are comparable to the participating centers, which supports the feasibility claim.

What the paper does well is mostly honesty and clarity. The federation setup is described in enough detail to follow, they use both FedAvg and FedProx with a clear rationale, and they openly discuss the registration problem from the start. The qualitative figures are useful, and the data availability statement points to a public dataset.

The soft spots are real but not fatal. The biggest one is the registration confound, and the paper essentially concedes it: they say no objective method is available to quantify registration errors, and they attribute Center B's lower scores and Center C's higher scores to unquantified differences in misalignment. That means the rank order in Table 2 may reflect preprocessing difficulty as much as model generalization. The stress-test note is right that this weakens the central claim. The fix is not impossible — they could use a registration-quality surrogate, or at least report metrics on a subset they judge to be well-aligned, but as written the quantitative evidence for cross-center comparability is shaky.

Second, there are no baselines. No centralized training on all three centers combined, no single-center models, no FedAvg-only comparison. Without those, we don't know whether federation costs accuracy or how much robustness it actually buys. That is a fair demand for a first application.

Third, only four test patients per participating center. The standard deviations are large, and no statistical tests are run, so the per-center differences could easily be noise.

On the privacy claim: it's standard FL framing, not exaggerated beyond the field's norm, but it would be nice if they acknowledged that sharing model updates and preprocessing parameters still leaks some information.

Overall, this is a useful feasibility study with an honest limitation section. It deserves a serious referee because it breaks new ground, even if the contribution is application-level. I'd send it to review, but the revision should add centralized baselines and a more principled treatment of the registration confound.

My recommendation: engage with it, but read Table 2 as "preprocessed-data similarity under federation" rather than as proof of generalized CBCT-to-sCT quality.

Referee Report

3 major / 7 minor

Summary. The paper proposes a cross-silo federated learning framework for CBCT-to-synthetic CT (sCT) translation in the head and neck, extending the authors' previous FedSynthCT-Brain work to a new imaging modality. A Pix2Pix conditional GAN is trained on three European centers from the SynthRAD2025 challenge (Centers B, C, E) using FedAvg with FedProx, with Center A held out as an external test set of 60 patients and four test patients per participating center. The reported image similarity metrics (Table 2) show Center A achieving MAE 75.22 ± 11.81 HU, SSIM 0.904 ± 0.034, and PSNR 33.52 ± 2.06 dB, which the authors interpret as evidence of effective cross-center generalization without fine-tuning. The conclusion focuses on the technical feasibility of privacy-preserving federated training for CBCT-to-sCT synthesis.

Significance. If the results are substantiated, this work provides a valuable proof-of-concept that federated learning can support multi-institutional training for CBCT-to-sCT synthesis, a clinically relevant task where data sharing is constrained by privacy regulations. The external validation on 60 patients from an unseen center is a strong design element, and the use of the public SynthRAD2025 dataset supports reproducibility. However, the absence of centralized or single-center baselines, the small test set per participating center (n=4), and the unquantified registration confound currently limit the strength of the generalization claims and prevent the paper from demonstrating an advantage over standard training.

major comments (3)

[Section 4, Table 2] The claim that the federated model 'generalized effectively' is not supported by any comparison with a centralized model trained on pooled data or with single-center models. Without these baselines, the reader cannot determine whether federated training yields a benefit or incurs a performance loss relative to standard training. Please report the same metrics for a centralized baseline and/or single-center baselines using the same architecture, preprocessing, and hyperparameters.
[Section 3.2 and Section 4] Each participating center contributes only four test patients, and the reported standard deviations are large (e.g., Center C MAE = 64.38 ± 13.63 HU). No statistical tests are performed to assess whether the observed differences across centers are meaningful, so the cross-center comparison in Table 2 could be dominated by sampling variability. Please provide per-patient results and appropriate statistical comparisons (e.g., confidence intervals or paired tests against a baseline), or explicitly limit the generalization claim to the external Center A cohort.
[Section 4] The paper acknowledges that residual misregistration between CBCT and CT is the primary source of error and attributes Center B's lower scores to 'a higher prevalence of registration issues,' but it also states that no objective method is available to quantify registration errors. Because the test data were deformably registered and the metrics are computed on the registered images, the observed differences among centers could reflect registration quality rather than model generalization. Please provide a quantitative proxy for registration quality (e.g., metrics computed before and after registration, or an independent alignment measure) or substantially temper the cross-center generalization claims.

minor comments (7)

[Section 3.5] The text refers to 'Centers B, C, and D' but Center D was excluded in Section 3.1; this should read 'Centers B, C, and E.'
[Section 3.2] The deformable registration algorithm applied to the test data is not described; please specify the method and its parameters so that the evaluation pipeline can be reproduced.
[Section 3.4] The number of federated aggregation rounds is not reported, which makes it impossible to assess convergence or computational cost; please add this information.
[Section 3.3] Equation (3) defines Lpixel as MAE(CT - sCT) but the text calls it Masked MAE; please clarify that the metric is computed within the body mask, consistent with Section 3.5.
[Section 3.2] There is a typo in 'infrastracture'; it should be 'infrastructure.'
[Section 5] The statement that 'no objective and independent method is available' to quantify registration errors is overly strong; consider rewording to indicate that no such method was applied in the current study.
[Abstract] The phrase 'comparable performance' on the external validation set is ambiguous; please specify that it is comparable to the participating centers or provide the benchmark explicitly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the generalization claim rests on held-out and external inference metrics, not on fitted parameters or a self-citation chain.

full rationale

The central claim—that the federated model generalizes across participating centers and to external Center A—is supported by Table 2 metrics computed on test patients never used for training, including 60 external Center A cases excluded from the federation. No parameter was fitted to those test sets; the reported MAE, SSIM, and PSNR values are observed outcomes. The self-citations to FedSynthCT-Brain [9] concern methodological choices (FedProx aggregation and the value of mu), not the quantitative generalization results, and the model is also evaluated independently on external data. Hyperparameters such as lambda_pixel=100 come from the external Pix2Pix source [18]. The paper's statement in Section 4 that no objective and independent method is available to quantify registration errors is an internal-validity caveat about residual misregistration potentially confounding cross-center metric differences, but it is not circularity: the metrics still measure the model plus preprocessing pipeline on unseen data rather than reproducing an input by construction. No equation reduces to its own inputs, and no fitted value is renamed as a prediction. The derivation is therefore self-contained against an external benchmark.

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

The central claim is an empirical feasibility result, not a derivation. It relies on a small number of inherited hyperparameters and empirically chosen intensity ranges, plus domain assumptions about registration and metric validity. No new physical or conceptual entities are introduced.

free parameters (4)

Generator L1 loss weight λ_pixel = 100
Taken from Pix2Pix [18] and applied unchanged; controls the balance between adversarial and pixel loss in Eq. (4).
FedProx proximal coefficient μ = 3
Set according to the authors' prior FedSynthCT-Brain study [9] and used in Eq. (6); affects how strongly local updates are penalized.
CBCT LUT normalization range = [-800, 2000] HU
Empirically chosen per client by analyzing local intensity distributions (Section 3.2); standardizes input intensities across sites.
CT clipping range = [-1000, 1500] HU
Empirically chosen to mitigate extreme values from metal implants (Section 3.2).

assumptions (4)

domain assumption CBCT-CT pairs are sufficiently aligned after challenge preprocessing and the additional deformable registration to make per-voxel losses and metrics meaningful.
The paper applies masked MAE loss and metrics on registered pairs; Section 3.2 states additional deformable registration was applied, and Section 4 admits residual misregistration is the main error source and is not quantified.
domain assumption SynthRAD2025 public dataset centers are representative of real multi-institutional head-and-neck CBCT/CT practice.
Sections 3.1 and 5 generalize from four centers to institutional heterogeneity and clinical scalability.
domain assumption Image-similarity metrics (MAE, SSIM, PSNR) are sufficient evidence for synthetic CT quality in this study.
Section 5 explicitly notes that dose calculation was not evaluated because planning data were unavailable, so the clinical utility of the reported metrics is assumed rather than measured.
standard math Standard Pix2Pix cGAN training and FedAvg/FedProx aggregation behave as described in the cited literature.
Sections 3.3 and 3.4 rely on [18] and [32] without re-deriving convergence or stability properties.

how reviews work

0 comments

Cite this review

Pith. "Pith review of A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck." pith.science (2026). https://pith.science/paper/LGQRIA7Y

@misc{pith2026250608654,
  author       = {Pith},
  title        = {Pith review of: A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LGQRIA7Y}},
  note         = {Machine review of arXiv:2506.08654}
}

abstract

Shortened Abstract Cone-beam computed tomography (CBCT) has become a widely adopted modality for image-guided radiotherapy (IGRT). However, CBCT suffers from increased noise, limited soft-tissue contrast, and artifacts, resulting in unreliable Hounsfield unit values and hindering direct dose calculation. Synthetic CT (sCT) generation from CBCT addresses these issues, especially using deep learning (DL) methods. Existing approaches are limited by institutional heterogeneity, scanner-dependent variations, and data privacy regulations that prevent multi-center data sharing. To overcome these challenges, we propose a cross-silo horizontal federated learning (FL) approach for CBCT-to-sCT synthesis in the head and neck region, extending our FedSynthCT framework. A conditional generative adversarial network was collaboratively trained on data from three European medical centers in the public SynthRAD2025 challenge dataset. The federated model demonstrated effective generalization across centers, with mean absolute error (MAE) ranging from $64.38\pm13.63$ to $85.90\pm7.10$ HU, structural similarity index (SSIM) from $0.882\pm0.022$ to $0.922\pm0.039$, and peak signal-to-noise ratio (PSNR) from $32.86\pm0.94$ to $34.91\pm1.04$ dB. Notably, on an external validation dataset of 60 patients, comparable performance was achieved (MAE: $75.22\pm11.81$ HU, SSIM: $0.904\pm0.034$, PSNR: $33.52\pm2.06$ dB) without additional training, confirming robust generalization despite protocol, scanner differences and registration errors. These findings demonstrate the technical feasibility of FL for CBCT-to-sCT synthesis while preserving data privacy and offer a collaborative solution for developing generalizable models across institutions without centralized data sharing or site-specific fine-tuning.

Figures

Figures reproduced from arXiv: 2506.08654 by the authors.

**Figure 2.** Qualitative results for the best and worst sCT cases from Center B, which showed lower image similarity [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

**Figure 3.** Visual comparison of the best and worst sCT cases from Center C. Compared to the other centres, Center C [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Visual evaluation of the best and worst sCT cases from Center E. Despite the use of a different scanner [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

33 extracted references · 31 canonical work pages

[1]

Luca Boldrini, Andrea D’Aviero, Francesca De Felice, Isacco Desideri, Roberta Grassi, Carlo Greco, Giuseppe Carlo Iorio, Valerio Nardone, Antonio Piras, and Viola Salvestrini. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the young group of the italian association of radiotherapy and clinical oncology (ya...

work page 2023
[2]

Synthetic CT Image Generation From CBCT: A systematic review

Alzahra Altalib, Scott McGregor, Chunhui Li, and Alessandro Perelli. Synthetic CT Image Generation From CBCT: A systematic review. IEEE Transactions on Radiation and Plasma Medical Sciences, pages 1–1, 2025

work page 2025
[3]

Deep learning based synthetic-CT generation in radiotherapy and PET: A review

Maria Francesca Spadea, Matteo Maspero, Paolo Zaffino, and Joao Seco. Deep learning based synthetic-CT generation in radiotherapy and PET: A review. Medical Physics, 48(11):6537–6566, August 2021

work page 2021
[4]

Current state and future applications of radiological image guidance for particle therapy

Guillaume Landry and Chia-ho Hua. Current state and future applications of radiological image guidance for particle therapy. Medical Physics, 45(11), November 2018

work page 2018
[5]

Artificial intelligence for treatment delivery: image-guided radiotherapy

Moritz Rabe, Christopher Kurz, Adrian Thummerer, and Guillaume Landry. Artificial intelligence for treatment delivery: image-guided radiotherapy. Strahlentherapie und Onkologie, 201(3):283 – 297, 2025

work page 2025
[6]

Personalizing radiotherapy with adaptive radiotherapy: Interest and challenges

Olivier Riou, Jessica Prunaretty, and Morgan Michalet. Personalizing radiotherapy with adaptive radiotherapy: Interest and challenges. Cancer/Radiothérapie, 28(6):603–609, 2024. 35e congrès de la Société française de radiothérapie oncologique

work page 2024
[7]

A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer

Edoardo Mastella, Francesca Calderoni, Luigi Manco, Martina Ferioli, Serena Medoro, Alessandro Turra, Melchiore Giganti, and Antonio Stefanelli. A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer. Physics and Imaging in Radiation Oncology, 33:100731, January 2025

work page 2025
[8]

Huijben, Maarten L

Evi M.C. Huijben, Maarten L. Terpstra, Arthur Jr. Galapon, Suraj Pai, Adrian Thummerer, Peter Koopmans, Manya Afonso, Maureen van Eijnatten, Oliver Gurney-Champion, Zeli Chen, Yiwen Zhang, Kaiyi Zheng, Chuanpu Li, Haowen Pang, Chuyang Ye, Runqi Wang, Tao Song, Fuxin Fan, Jingna Qiu, Yixing Huang, Juhyung Ha, Jong Sung Park, Alexandra Alain-Beaudoin, Silva...

work page 2024

Show all 33 references

[9]

FedSynthCT-Brain: A federated learning framework for multi-institutional brain mri-to-ct synthesis

Ciro Benito Raggio, Mathias Krohmer Zabaleta, Nils Skupien, Oliver Blanck, Francesco Cicone, Giuseppe Lucio Cascini, Paolo Zaffino, Lucia Migliorelli, and Maria Francesca Spadea. FedSynthCT-Brain: A federated learning framework for multi-institutional brain mri-to-ct synthesis...

2025
[10]

Secure, privacy-preserving and federated machine learning in medical imaging

Georgios A Kaissis, Marcus R Makowski, Daniel Rückert, and Rickmer F Braren. Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6):305–311, 2020

2020
[11]

Federated learning for medical image analysis: A survey

Hao Guan, Pew-Thian Yap, Andrea Bozoki, and Mingxia Liu. Federated learning for medical image analysis: A survey. Pattern Recognition, 151:110424, 2024

2024
[12]

Medical imaging applications of federated learning

Sukhveer Singh Sandhu, Hamed Taheri Gorji, Pantea Tavakolian, Kouhyar Tavakolian, and Alireza Akhbardeh. Medical imaging applications of federated learning. Diagnostics, 13(19):3140, October 2023

2023
[13]

Mirza, Gokberk Elmas, Muzaffer Ozbey, Salman U.H

Onat Dalmaz, Muhammad U. Mirza, Gokberk Elmas, Muzaffer Ozbey, Salman U.H. Dar, Emir Ceyani, Kader K. Oguz, Salman Avestimehr, and Tolga Çukur. One model to unite them all: Personalized federated learning of multi-contrast mri synthesis. Medical Image Analysis, 94:103121, 2024

2024
[14]

Alison Noble

Netzahualcoyotl Hernandez-Cruz, Pramit Saha, Md Mostafa Kamal Sarker, and J. Alison Noble. Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis. Big Data and Cognitive Computing, 8(9):99, August 2024. 11 C.B. Raggio et al. - Privacy-Preser...

2024
[15]

Adrian Thummerer, Erik van der Bijl, Arthur Jr Galapon, Florian Kamp, Mark Savenije, Christina Muijs, Shafak Aluwini, Roel J. H. M. Steenbakkers, Stephanie Beuel, Martijn P. W. Intven, Johannes A. Langendijk, Stefan Both, Stefanie Corradini, Viktor Rogowski, Maarten Terpstra, ...

2025
[16]

Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review

Branimir Rusanov, Ghulam Mubashar Hassan, Mark Reynolds, Mahsheed Sabet, Jake Kendrick, Pejman Row- shanfarzad, and Martin Ebert. Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review. Medical Physics, 49(9):6019–...

2022
[17]

U-net: Convolutional networks for biomedical image segmentation

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Nassir Navab, Joachim Hornegger, William M. Wells, and Alejandro F. Frangi, editors,Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015,...

2015
[18]

Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. Image-to-image translation with conditional adversarial networks, 2018

2018
[19]

A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy

Xinyuan Chen, Yuxiang Liu, Bining Yang, Ji Zhu, Siqi Yuan, Xuejie Xie, Yueping Liu, Jianrong Dai, and Kuo Men. A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy. Frontiers in Oncology, V olume 12 - 2022, 2022

2022
[20]

Synthetic computed tomography for low-field magnetic resonance-only radiotherapy in head-and-neck cancer using residual vision transformers

Agustina La Greca Saint-Esteven, Ricardo Dal Bello, Mariia Lapaeva, Lisa Fankhauser, Bertrand Pouymayou, Ender Konukoglu, Nicolaus Andratschke, Panagiotis Balermpas, Matthias Guckenberger, and Stephanie Tanadini- Lang. Synthetic computed tomography for low-field magnetic reson...

2023
[21]

Multi-Institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation

Micah J Sheller, G Anthony Reina, Brandon Edwards, Jason Martin, and Spyridon Bakas. Multi-Institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. Brainlesion, 11383:92–104, January 2019

2019
[22]

Jorge Cardoso, and Andrew Feng

Wenqi Li, Fausto Milletarì, Daguang Xu, Nicola Rieke, Jonny Hancox, Wentao Zhu, Maximilian Baust, Yan Cheng, Sébastien Ourselin, M. Jorge Cardoso, and Andrew Feng. Privacy-preserving federated brain tumour segmentation. In Heung-Il Suk, Mingxia Liu, Pingkun Yan, and Chunfeng L...

2019
[23]

Fed-Sim: Federated simulation for medical imaging, 2020

Daiqing Li, Amlan Kar, Nishant Ravikumar, Alejandro F Frangi, and Sanja Fidler. Fed-Sim: Federated simulation for medical imaging, 2020

2020
[24]

Synthetic learning: Learn from distributed asynchronized discriminator gan without sharing medical image data, 2020

Qi Chang, Hui Qu, Yikai Zhang, Mert Sabuncu, Chao Chen, Tong Zhang, and Dimitris Metaxas. Synthetic learning: Learn from distributed asynchronized discriminator gan without sharing medical image data, 2020

2020
[25]

Communication- Efficient Learning of Deep Networks from Decentralized Data

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication- Efficient Learning of Deep Networks from Decentralized Data. In Aarti Singh and Jerry Zhu, editors, Proceedings of the 20th International Conference on Artificial Intelligence ...

2017
[26]

FedMed- GAN: Federated domain translation on unsupervised cross-modality brain image synthesis

Jinbao Wang, Guoyang Xie, Yawen Huang, Jiayi Lyu, Feng Zheng, Yefeng Zheng, and Yaochu Jin. FedMed- GAN: Federated domain translation on unsupervised cross-modality brain image synthesis. Neurocomputing, 546:126282, 2023

2023
[27]

Galapon, Florian Kamp, and Matteo Maspero

Adrian Thummerer, Erik van der Bijl, Arthur Jr. Galapon, Florian Kamp, and Matteo Maspero. SynthRAD2025 Grand Challenge dataset: Training, 2025

2025
[28]

Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Javier Fernandez-Marques, Yan Gao, Lorenzo Sani, Kwing Hei Li, Titouan Parcollet, Pedro Porto Buarque de Gusmão, and Nicholas D

Daniel J. Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Javier Fernandez-Marques, Yan Gao, Lorenzo Sani, Kwing Hei Li, Titouan Parcollet, Pedro Porto Buarque de Gusmão, and Nicholas D. Lane. Flower: A Friendly Federated Learning Research Framework, 2022

2022
[29]

PyTorch: an imperative style, high-performance deep learning library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fan...

2019
[30]

M. Jorge Cardoso, Wenqi Li, Richard Brown, Nic Ma, Eric Kerfoot, Yiheng Wang, Benjamin Murrey, Andriy Myronenko, Can Zhao, Dong Yang, Vishwesh Nath, Yufan He, Ziyue Xu, Ali Hatamizadeh, Andriy Myronenko, Wentao Zhu, Yun Liu, Mingxin Zheng, Yucheng Tang, Isaac Yang, Michael Zep...

2025
[31]

Normalization techniques for CNN based analysis of surgical cone beam ct volumes

Celia Martín Vicario, Florian Kordon, Felix Denzinger, Jan Siad El Barbari, Maxim Privalov, Jochen Franke, Andreas Maier, and Holger Kunze. Normalization techniques for CNN based analysis of surgical cone beam ct volumes. Medical Imaging 2022: Image Processing, page 85, March 2022

2022
[32]

Federated optimization in heterogeneous networks, 2020

Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated optimization in heterogeneous networks, 2020

2020
[33]

Jehadeesan, B

Vikas Kumar, Manoj Sharma, R. Jehadeesan, B. Venkatraman, and Debdoot Sheet. Simulating cross-modal medical images using multi-task adversarial learning of a deep convolutional neural network. International Journal of Imaging Systems and Technology, 34(4):e23113, 2024. 13

2024

Pith tools

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

[1] [1]

Luca Boldrini, Andrea D’Aviero, Francesca De Felice, Isacco Desideri, Roberta Grassi, Carlo Greco, Giuseppe Carlo Iorio, Valerio Nardone, Antonio Piras, and Viola Salvestrini. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the young group of the italian association of radiotherapy and clinical oncology (ya...

work page 2023

[2] [2]

Synthetic CT Image Generation From CBCT: A systematic review

Alzahra Altalib, Scott McGregor, Chunhui Li, and Alessandro Perelli. Synthetic CT Image Generation From CBCT: A systematic review. IEEE Transactions on Radiation and Plasma Medical Sciences, pages 1–1, 2025

work page 2025

[3] [3]

Deep learning based synthetic-CT generation in radiotherapy and PET: A review

Maria Francesca Spadea, Matteo Maspero, Paolo Zaffino, and Joao Seco. Deep learning based synthetic-CT generation in radiotherapy and PET: A review. Medical Physics, 48(11):6537–6566, August 2021

work page 2021

[4] [4]

Current state and future applications of radiological image guidance for particle therapy

Guillaume Landry and Chia-ho Hua. Current state and future applications of radiological image guidance for particle therapy. Medical Physics, 45(11), November 2018

work page 2018

[5] [5]

Artificial intelligence for treatment delivery: image-guided radiotherapy

Moritz Rabe, Christopher Kurz, Adrian Thummerer, and Guillaume Landry. Artificial intelligence for treatment delivery: image-guided radiotherapy. Strahlentherapie und Onkologie, 201(3):283 – 297, 2025

work page 2025

[6] [6]

Personalizing radiotherapy with adaptive radiotherapy: Interest and challenges

Olivier Riou, Jessica Prunaretty, and Morgan Michalet. Personalizing radiotherapy with adaptive radiotherapy: Interest and challenges. Cancer/Radiothérapie, 28(6):603–609, 2024. 35e congrès de la Société française de radiothérapie oncologique

work page 2024

[7] [7]

A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer

Edoardo Mastella, Francesca Calderoni, Luigi Manco, Martina Ferioli, Serena Medoro, Alessandro Turra, Melchiore Giganti, and Antonio Stefanelli. A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer. Physics and Imaging in Radiation Oncology, 33:100731, January 2025

work page 2025

[8] [8]

Huijben, Maarten L

Evi M.C. Huijben, Maarten L. Terpstra, Arthur Jr. Galapon, Suraj Pai, Adrian Thummerer, Peter Koopmans, Manya Afonso, Maureen van Eijnatten, Oliver Gurney-Champion, Zeli Chen, Yiwen Zhang, Kaiyi Zheng, Chuanpu Li, Haowen Pang, Chuyang Ye, Runqi Wang, Tao Song, Fuxin Fan, Jingna Qiu, Yixing Huang, Juhyung Ha, Jong Sung Park, Alexandra Alain-Beaudoin, Silva...

work page 2024