Pith. sign in

REVIEW 3 major objections 3 minor 67 references

Hybrid Compact Least-Squares and Central Weighted Essentially Non-Oscillatory Schemes for Hyperbolic Conservation Laws on Structured Curvilinear Grids

T0 review · 3 major / 3 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read The paper proposes third- and fifth-order hybrid finite volume schemes that switch between compact least-squares reconstruction in smooth regions and central WENO at discontinuities, applied to compressible flows on structured curvilinear…

desk verdict The abstract and the body are two different papers: the claimed compact least-squares/CWENO schemes have no derivation or benchmarks in the supplied text, so this submission cannot be reviewed as a CFD paper. read the letter →

arxiv 2508.02042 v1 pith:OHFTCITI submitted 2025-08-04 physics.flu-dyn physics.comp-ph

Jianhua Pan , Luxin Li , Ji Yin , Wei-Gang Zeng This is my paper
classification physics.flu-dynphysics.comp-ph MSC 65M0876M1276N15 PACS 47.11.Df47.40.-x
keywords hyperbolicconservationlawsfinitevolumemethodcompactleast-squarescentralWENOcurvilinearstructuredgridsshockcapturingcompressibleflowshigh-orderschemes
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 argues that a finite volume method for hyperbolic conservation laws can get both high resolution of smooth multiscale structure and oscillation-free shock capture on curved, body-fitted grids by switching reconstructions. In smooth regions the scheme uses compact least-squares reconstruction built from interface differences of derivatives; near discontinuities it switches to central weighted essentially non-oscillatory (CWENO) reconstruction. Third- and fifth-order versions are proposed, with free parameters tuned to widen the range of resolved wave numbers at controlled dissipation. The reconstruction comes from solving block tridiagonal linear systems, producing an explicit polynomial per control volume at computational cost described as acceptable relative to pure CWENO. Benchmark evidence spans one- and two-dimensional, linear and nonlinear, inviscid and viscous problems on uniform and nonuniform curvilinear grids, targeting compressible flows that contain both multiscale structures and shocks.

What carries the argument

The central object is the hybrid reconstruction operator inside a finite volume discretization on structured curvilinear grids. The compact least-squares branch uses interfacial differences of derivatives to build high-order polynomials with wide resolved bandwidth, the central WENO branch provides oscillation-free behavior near shocks, and a shock detector from previous work selects the branch, treating smooth first-order extrema as smooth rather than discontinuous. Solving the resulting block tridiagonal reconstruction linear systems gives an explicit polynomial for every control volume, which is what makes the hybrid practically efficient.

What would settle it

A manufactured-smooth-solution convergence test on a strongly curved, nonuniform grid, paired with a shock-entropy wave interaction test, would settle the claim: if the scheme does not reach the designed third or fifth order in smooth regions, or overshoots at the shock, the hybrid fails. The detector's role can be isolated by feeding it a smooth first-order extremum and a weak discontinuity on the same curvilinear grid and checking that it labels them differently.

Watch

Extended reading notes

Core claim

The central claim is that a hybrid scheme can preserve broad resolved bandwidth in smooth regions without giving up non-oscillatory shock capture. The compact least-squares branch handles smooth structure through interface differences of derivatives; the central WENO branch activates at discontinuities; a shock detector built in earlier work decides which branch to use and is asserted to recognize smooth first-order extrema as smooth. Solving block tridiagonal reconstruction systems yields an explicit polynomial for each control volume, and the paper reports that this overhead is acceptable compared with pure central WENO, with third- and fifth-order versions demonstrated on compressible test problems.

Load-bearing premise

The hybrid scheme's success rests on the shock detector taken from the authors' earlier work: it must tell smooth first-order extrema apart from genuine discontinuities on a curvilinear grid, and if it misclassifies either, the scheme will switch stencils at the wrong place and lose accuracy or stability.

Editorial extensions

If this is right

  • Third- and fifth-order finite volume schemes can resolve multiscale flow structure and capture shocks on the same curvilinear grid, easing the usual accuracy-versus-robustness trade-off.
  • The block tridiagonal reconstruction keeps the extra cost modest enough that the hybrid remains practical compared with running pure central WENO.
  • Users can tune the free parameters to trade resolved bandwidth against dissipation, matching the scheme to the scale content of a particular flow.
  • The same framework is claimed to work for linear and nonlinear, inviscid and viscous problems in one and two dimensions on uniform and nonuniform curvilinear grids.

Reading between the lines

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

  • If the shock detector is as robust as claimed, the same detector-plus-hybrid structure could be reused at other orders or on adaptive grids, because the switching logic is independent of the reconstruction stencils.
  • A direct comparison of the compact least-squares branch against high-order finite-difference WENO on a smooth turbulence-like test would show whether the optimized free parameters deliver the promised bandwidth advantage in practice.
  • The full text supplied with this abstract is a different manuscript, so the derivations, block tridiagonal details, and benchmark tables behind these claims were not available in this pass; they should be checked against the actual article.
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.

Referee Report

3 major / 3 minor

Summary. The manuscript submitted under arXiv:2508.02042 consists of an abstract describing third- and fifth-order hybrid compact least-squares central weighted essentially non-oscillatory (CLS-CWENO) finite volume schemes for hyperbolic conservation laws on curvilinear structured grids, followed by a full text that is a different paper: "Conditional Diffusion Model with Anatomical-Dose Dual Constraints for End-to-End Multi-Tumor Dose Prediction" (arXiv:2508.02043v2). The body contains no derivation of the CLS-CWENO reconstruction, no CWENO weights or smoothness indicators, no curvilinear metric formulation, no block tridiagonal linear system, no shock detector, and no conservation-law benchmark. The central claims of the abstract are therefore completely unsupported by the submitted text.

Significance. If the abstract's claims were substantiated, the work could be significant: hybrid schemes that maintain high resolution of multiscale smooth structures while capturing shocks on curvilinear grids, with acceptable overhead relative to pure CWENO, would be of practical interest in compressible-flow CFD. The shock detector's ability to distinguish smooth first-order extrema from discontinuities would be a valuable and nontrivial robustness property. However, none of this is established in the submitted document. There are no machine-checked proofs, reproducible code, or parameter-free derivations to offset the absence. Under the stated review rule that all supplied text is in-scope evidence, the supplied body is unrelated to the advertised subject, so the claimed contribution is not reviewable in its present form.

major comments (3)
  1. [Full Text (Sections 1–5)] The full text under this ID is the ADDiff-Dose manuscript; Equations (1)–(9) and Algorithm 1 define a conditional diffusion model for radiotherapy dose prediction, with nothing on compact least-squares reconstruction or CWENO. The abstract's statement that "a series of third- and fifth-order hybrid compact least-squares central weighted essentially non-oscillatory schemes are proposed" is therefore a claim without any derivation, formulation, or validation in the manuscript.
  2. [Abstract] The abstract asserts that a shock detector from previous work is "introduced and validated"; in the supplied body no shock detector is defined or tested. Since the hybrid scheme's accuracy and stability depend on correct identification of discontinuities, this missing content is load-bearing and cannot be assessed.
  3. [Abstract] The claim that free parameters in the compact least-squares schemes are "firstly optimized" cannot be checked: no stencil, no objective function, no optimization procedure, and no resolved-bandwidth analysis appear anywhere in the text. Without the formulation, it is impossible to determine whether the parameters are derived or fitted.
minor comments (3)
  1. [Title page] The title of the submitted document does not match the title in the abstract; the header identifies the manuscript as arXiv:2508.02043v2, suggesting an ID mismatch that must be resolved before review.
  2. [Keywords and MSC codes] The MSC codes "00-01, 99-00" and the keywords are from the radiotherapy paper and are not appropriate for the advertised conservation-law topic.
  3. [References] References [20]–[67] and Supplementary Tables 1–3 are all about dose prediction and provide no support for the CLS-CWENO claims; the bibliography is irrelevant to the advertised subject.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity is assessable or demonstrable: the supplied full text does not contain the compact least-squares/CWENO derivation claimed in the abstract, so there is no equation chain to reduce to its own inputs.

full rationale

The abstract of arXiv:2508.02042 claims a family of third- and fifth-order hybrid compact least-squares central WENO schemes for hyperbolic conservation laws on curvilinear structured grids, including optimized free parameters, a shock detector from prior work, and block tridiagonal reconstruction. However, the full text supplied under this ID is actually the manuscript of arXiv:2508.02043v2, a conditional diffusion model for radiotherapy dose prediction. None of the claimed compact least-squares reconstruction, CWENO weights, curvilinear metric terms, block tridiagonal system, shock-detector formulation, or conservation-law benchmarks appears in the body. Circularity, per the review rules, requires exhibiting a specific reduction such as an equation defined in terms of another equation, or a fitted parameter renamed as a prediction. No such reduction can be quoted because the relevant derivations and equations are absent from the supplied manuscript. The only apparent self-citation is the abstract's statement that 'a shock detector proposed in our previous work is introduced and validated,' but with the detector's mathematical definition omitted, there is no basis to claim that it is defined in terms of the scheme's outputs or that the scheme's performance is forced by its own construction. Absence of derivation is a completeness and reviewability concern, not a circularity concern, and the default honest finding under the hard rules is therefore no significant circularity, score 0. The supplied text's own limitations section acknowledges untested generalizability and sensitivity to segmentation accuracy, which are ordinary caveats rather than circular reasoning. Thus no circular step is established.

Assumptions & free parameters 1 free parameters · 2 assumptions · 0 invented entities

The central claim rests on the finite volume framework and on the reliability of a previously published shock detector. The abstract also mentions optimized free parameters in the compact least-squares stencils. The full text mismatch means these are inferred from the abstract alone. Entities proposed in the unrelated full text (e.g., LightweightVAE3D, ADDiff-Dose) are not counted because they belong to a different paper.

free parameters (1)
  • compact least-squares stencil weights
    The abstract states free parameters were optimized to reach a broad range of resolved bandwidths; their values are not given in the abstract and the body does not discuss them.
assumptions (2)
  • domain assumption The finite volume framework with curvilinear structured grids supports the claimed compact least-squares reconstruction.
    The abstract assumes a standard finite volume discretization on curvilinear grids.
  • ad hoc to paper The previously proposed shock detector reliably distinguishes smooth first-order extrema from discontinuities.
    This is a premise from the authors' previous work and is load-bearing for the hybrid switching behavior; it is not independently established in this submission.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Hybrid Compact Least-Squares and Central Weighted Essentially Non-Oscillatory Schemes for Hyperbolic Conservation Laws on Structured Curvilinear Grids." pith.science (2026). https://pith.science/paper/OHFTCITI

@misc{pith2026250802042,
  author       = {Pith},
  title        = {Pith review of: Hybrid Compact Least-Squares and Central Weighted Essentially Non-Oscillatory Schemes for Hyperbolic Conservation Laws on Structured Curvilinear Grids},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OHFTCITI}},
  note         = {Machine review of arXiv:2508.02042}
}
read the original abstract

A series of third- and fifth-order hybrid compact least-squares central weighted essentially non-oscillatory schemes are proposed and applied to curvilinear structured grids for the finite volume method. In smooth regions, compact least-squares schemes based on interfacial differences of derivatives are utilized to keep the high resolution of multiscale structures, whereas in discontinuous regions, central weighted essentially non-oscillatory schemes are included to enable the oscillation free shock capturing capability of the hybrid schemes. Free parameters in the proposed compact least-squares schemes are firstly optimized to reach a broad range of resolved bandwidths with different levels of dissipation. In addition, a shock detector proposed in our previous work is introduced and validated to be not only able to detect smooth first-order extrema but also robust in detecting discontinuities. Through the solution of block tridiagonal reconstruction linear systems, the resulting schemes can give an explicit polynomial for each control volume and are efficient with an acceptable computational overhead when compared to the pure central weighted essentially non-oscillatory schemes. Eventually, benchmarks including one-dimensional and two-dimensional, linear and nonlinear, inviscid and viscous problems on uniform and nonuniform curvilinear grids demonstrate the proposed schemes' promising applicability in compressible flows which have both multiscale structures and discontinuities.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

67 extracted references · 52 canonical work pages

  1. [1]

    R. H. Mak, M. G. Endres, J. H. Paik, R. A. Sergeev, H. Aerts, C. L. Williams, K. R. Lakhani, E. C. Guinan, Use of crowd innovation to develop an artificial intelligence–based solution for radiation therapy targeting, JAMA Oncology 5 (5) (2019) 654–661

    work page 2019

  2. [2]

    Minniti, C

    G. Minniti, C. Goldsmith, M. Brada, Radiotherapy, in: M. J. Aminoff, F. Boller, D. F. Swaab (Eds.), Handbook of Clinical Neurology, Vol. 104, Elsevier, 2012, pp. 215–228

    work page 2012

  3. [3]

    C.Jiang, T.Ji, Q.Qiao, Applicationandprogressofartificialintelligenceinradiationtherapydoseprediction, Clinical and Translational Radiation Oncology 47 (2024) 100792

    work page 2024

  4. [4]

    X. Chen, K. Men, J. Zhu, B. Yang, M. Li, Z. Liu, X. Yan, J. Yi, J. Dai, Dvhnet: a deep learning-based prediction of patient-specific dose volume histograms for radiotherapy planning, Medical Physics 48 (6) (2021) 2705–2713

    work page 2021

  5. [5]

    X. Chen, K. Men, Y. Li, J. Yi, J. Dai, A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning, Medical Physics 46 (1) (2019) 56–64.doi:10.1002/mp.13262

    work page doi:10.1002/mp.13262 2019

  6. [6]

    Milletari, N

    F. Milletari, N. Navab, S.-A. Ahmadi, V-net: Fully convolutional neural networks for volumetric medical image segmentation, in: 2016 Fourth International Conference on 3D Vision (3DV), 2016, pp. 565–571. 22

    work page 2016

  7. [7]

    Y.Shao,X.Zhang,G.Wu,Q.Gu,J.Wang,Y.Ying,A.Feng,G.Xie,Q.Kong,Z.Xu,Predictionofthree-dimensional radiotherapyoptimaldosedistributionsforlungcancerpatientswithasymmetricnetwork,IEEEJournalofBiomedical and Health Informatics 25 (4) (2021) 1120–1127

    work page 2021

  8. [8]

    Z. Li, K. Chen, Z. Yang, Q. Zhu, X. Yang, Z. Li, J. Fu, A personalized dvh prediction model for hdr brachytherapy in cervical cancer treatment, Frontiers in Oncology 12 (2022) 967436

    work page 2022

Show all 67 references
  1. [9]

    S. H. Ahn, E. Kim, C. Kim, W. Cheon, M. Kim, S. B. Lee, Y. K. Lim, H. Kim, D. Shin, D. Y. Kim, J. H. Jeong, Deep learning method for prediction of patient-specific dose distribution in breast cancer, Radiation Oncology 16 (1) (2021) 154.doi:10.1186/s13014-021-01864-9

    2021 doi

  2. [10]

    Kontaxis, G

    C. Kontaxis, G. H. Bol, J. J. W. Lagendijk, B. W. Raaymakers, Deepdose: Towards a fast dose calculation engine for radiation therapy using deep learning, Physics in Medicine & Biology 65 (7) (2020) 075013

    2020

  3. [11]

    Z. Liu, J. Fan, J. Miao, Y. Tian, W. Hu, K. Men, J. Dai, Dose prediction for individualized prescription using deep learning: Modeling multiple radiotherapy clinical scenarios, Biomedical Signal Processing and Control 110 (2025) 108228

    2025

  4. [12]

    R.Gao,B.Lou,Z.Xu,D.Comaniciu,A.Kamen,Flexible-cmgan: Towardsprecise3ddosepredictioninradiotherapy, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 715–725

    2023

  5. [13]

    F. Shen, X. Du, Y. Gao, J. Yu, Y. Cao, X. Lei, J. Tang, Imagharmony: Controllable image editing with consistent object quantity and layout, arXiv preprint arXiv:2506.01949

  6. [14]

    F. Shen, J. Yu, C. Wang, X. Jiang, X. Du, J. Tang, Imaggarment-1: Fine-grained garment generation for controllable fashion design, arXiv preprint arXiv:2504.13176

    arXiv

  7. [15]

    F. Shen, H. Ye, J. Zhang, C. Wang, X. Han, W. Yang, Advancing pose-guided image synthesis with progressive conditional diffusion models (2024).arXiv:2310.06313. URLhttps://arxiv.org/abs/2310.06313

    2024 arXiv

  8. [16]

    F. Shen, C. Wang, J. Gao, Q. Guo, J. Dang, J. Tang, T.-S. Chua, Long-term talkingface generation via motion-prior conditional diffusion model, arXiv preprint arXiv:2502.09533

    arXiv

  9. [17]

    F. Shen, H. Ye, S. Liu, J. Zhang, C. Wang, X. Han, W. Yang, Boosting consistency in story visualization with rich-contextual conditional diffusion models (2024).arXiv:2407.02482. URLhttps://arxiv.org/abs/2407.02482

    2024 arXiv

  10. [18]

    F. Shen, J. Tang, Imagpose: A unified conditional framework for pose-guided person generation, in: Advances in Neural Information Processing Systems, Vol. 37, 2024, pp. 6246–6266

    2024

  11. [19]

    F. Shen, X. Jiang, X. He, H. Ye, C. Wang, X. Du, Z. Li, J. Tang, Imagdressing-v1: Customizable virtual dressing (2024).arXiv:2407.12705. URLhttps://arxiv.org/abs/2407.12705

    2024 arXiv

  12. [20]

    Zhang, C

    Y. Zhang, C. Li, L. Zhong, Z. Chen, W. Yang, X. Wang, Dosediff: Distance-aware diffusion model for dose prediction in radiotherapy, IEEE Transactions on Medical Imaging 43 (10) (2024) 3621–3633. 23

    2024

  13. [21]

    L. Fu, X. Li, X. Cai, Y. Wang, X. Wang, Y. Shen, Y. Yao, Md-dose: A diffusion model based on the mamba for radiation dose prediction, arXiv preprint arXiv:2403.08479

    arXiv

  14. [22]

    H. Sun, J. Qin, Z. Liu, X. Jia, K. Yan, L. Wang, Z. Liu, S. Gong, Generation driven understanding of localized 3d scenes with 3d diffusion model, Scientific reports 15 (1) (2025) 14385.doi:10.1038/s41598-025-98705-6. URLhttps://doi.org/10.1038/s41598-025-98705-6

    2025 doi

  15. [23]

    Vaswani, N

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, Attention is all you need, arXiv preprint arXiv:1706.03762

    arXiv

  16. [24]

    S. Foti, B. Koo, D. Stoyanov, M. J. Clarkson, 3d shape variational autoencoder latent disentanglement via mini-batch feature swapping for bodies and faces, arXiv preprint arXiv:2111.12448

    arXiv

  17. [25]

    Benton, Y

    J. Benton, Y. Shi, V. De Bortoli, G. Deligiannidis, A. Doucet, From denoising diffusions to denoising markov models, Journal of the Royal Statistical Society Series B: Statistical Methodology 86 (2) (2024) 286–301

    2024

  18. [26]

    B. He, T. Hofmann, Simplifying transformer blocks, arXiv preprint arXiv:2311.01906

    arXiv

  19. [27]

    A.Paszke,S.Gross,S.Chintala,G.Chanan,E.Yang,Z.DeVito,Z.Lin,A.Desmaison,L.Antiga,A.Lerer,Automatic differentiation in pytorch, 2017

    2017

  20. [28]

    Zhang, M

    H. Zhang, M. Cisse, Y. N. Dauphin, D. Lopez-Paz, mixup: Beyond empirical risk minimization, arXiv preprint arXiv:1710.09412

    arXiv

  21. [29]

    Alkinani, M

    H. Alkinani, M. El-Sakka, Patch-based models and algorithms for image denoising: a comparative review between patch-basedimagesdenoisingmethodsforadditivenoisereduction,EURASIPJournalonImageandVideoProcessing 2017 (1) (2017) 58

    2017

  22. [30]

    B.Wang, L.Teng, L.Mei, Z.Cui, X.Xu, Q.Feng, D.Shen, Deeplearning-basedheadandneckradiotherapyplanning dose prediction via beam-wise dose decomposition, in: L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, S. Li (Eds.), Medical Image Computing and Computer Assisted Intervention – M...

    2022

  23. [31]

    R.Gao,B.Lou,Z.Xu,D.Comaniciu,A.Kamen,Flexible-cmgan: Towardsprecise3ddosepredictioninradiotherapy, in: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 715–725

    2023

  24. [32]

    Babier, B

    A. Babier, B. Zhang, R. Mahmood, K. L. Moore, T. G. Purdie, A. L. McNiven, T. C. Y. Chan, Openkbp: The open-access knowledge-based planning grand challenge and dataset, Medical Physics 48 (9) (2021) 5549–5561

    2021

  25. [33]

    J.Bertels,D.Robben,R.Lemmens,D.Vandermeulen,Convolutionalneuralnetworksformedicalimagesegmentation, arXiv preprint arXiv:2211.09562

    arXiv

  26. [34]

    I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in: Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems, Vol. 27, Cur...

    2014

  27. [35]

    L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in: Proceedings of the European conference on computer vision (ECCV), 2018, pp. 801–818. 24

    2018

  28. [36]

    Kearney, J

    V. Kearney, J. W. Chan, S. Haaf, M. Descovich, T. D. Solberg, Dosenet: a volumetric dose prediction algorithm using 3d fully-convolutional neural networks, Physics in Medicine & Biology 63 (23) (2018) 235022

    2018

  29. [37]

    Dursun, L

    P. Dursun, L. Hong, G. Jhanwar, Q. Huang, Y. Zhou, J. Yang, H. Pham, L. Cervino, J. M. Moran, J. O. Deasy, et al., Automated vmat treatment planning using sequential convex programming: algorithm development and clinical implementation, Physics in Medicine & Biology 68 (15) (2...

    2023

  30. [38]

    G. D. Hugo, M. Rosu, Advances in 4d radiation therapy for managing respiration: part i–4d imaging, Zeitschrift für medizinische Physik 22 (4) (2012) 258–271

    2012

  31. [39]

    J. Fan, J. Wang, Z. Chen, C. Hu, Z. Zhang, W. Hu, Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique, Medical Physics 46 (1) (2019) 370–381

    2019

  32. [40]

    A. M. Barragán-Montero, D. Nguyen, W. Lu, M.-H. Lin, R. Norouzi-Kandalan, X. Geets, E. Sterpin, S. Jiang, Three- dimensional dose prediction for lung imrt patients with deep neural networks: robust learning from heterogeneous beam configurations, Medical Physics 46 (8) (2019) ...

    2019

  33. [41]

    B. Zhan, J. Xiao, C. Cao, X. Peng, C. Zu, J. Zhou, Y. Wang, Multi-constraint generative adversarial network for dose prediction in radiotherapy, Medical Image Analysis 77 (2022) 102339

    2022

  34. [42]

    J. Ma, D. Nguyen, T. Bai, M. Folkerts, X. Jia, W. Lu, L. Zhou, S. Jiang, A feasibility study on deep learning-based individualized 3d dose distribution prediction, Medical Physics 48 (8) (2021) 4438–4447

    2021

  35. [43]

    J. Ho, A. Jain, P. Abbeel, Denoising diffusion probabilistic models, Advances in Neural Information Processing Systems 33 (2020) 6840–6851

    2020

  36. [44]

    Y. He, P. Guo, Y. Tang, A. Myronenko, V. Nath, Z. Xu, D. Yang, C. Zhao, B. Simon, M. Belue, S. Harmon, B. Turkbey, D. Xu, W. Li, Vista3d: A unified segmentation foundation model for 3d medical imaging, arXiv preprint arXiv:2406.05285

    arXiv

  37. [45]

    van der Bijl, Y

    E. van der Bijl, Y. Wang, T. Janssen, S. Petit, Predicting patient specific pareto fronts from patient anatomy only, Radiotherapy and Oncology 150 (2020) 46–50

    2020

  38. [46]

    J. Dolz, I. Ben Ayed, C. Desrosiers, Dense multi-path u-net for ischemic stroke lesion segmentation in multiple image modalities, in: International MICCAI Brainlesion Workshop, Springer, 2018, pp. 271–280

    2018

  39. [47]

    S. H. Benedict, K. M. Yenice, D. Followill, J. M. Galvin, W. Hinson, B. Kavanagh, P. Keall, M. Lovelock, S. Meeks, L.Papiez, etal., Stereotacticbodyradiationtherapy: thereportofaapmtaskgroup101, MedicalPhysics37(8)(2010) 4078–4101

    2010

  40. [48]

    D.Rodriguez,T.Nayak,Y.Chen,R.Krishnan,Y.Huang,Ontheroleofdeeplearningmodelcomplexityinadversarial robustness for medical images, BMC Medical Informatics and Decision Making 22 (Suppl 2) (2022) 160

    2022

  41. [49]

    M. J. Sheller, B. Edwards, G. A. Reina, J. Martin, S. Pati, A. Kotrotsou, M. Milchenko, W. Xu, D. Marcus, R. R. Colen, et al., Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data, Scientific Reports 10 (1) (2020) 12598

    2020

  42. [50]

    Emami, J

    B. Emami, J. Lyman, A. Brown, L. Cola, M. Goitein, J. E. Munzenrider, B. Shank, L. J. Solin, M. Wesson, Tolerance of normal tissue to therapeutic irradiation, International Journal of Radiation Oncology* Biology* Physics 21 (1) (1991) 109–122. 25

    1991

  43. [51]

    Dosovitskiy, L

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., An image is worth 16x16 words: Transformers for image recognition at scale, arXiv preprint arXiv:2010.11929

    2010 arXiv

  44. [52]

    Nguyen, A

    D. Nguyen, A. S. Barkousaraie, C. Shen, X. Jia, S. Jiang, Generating pareto optimal dose distributions for radiation therapy treatment planning, in: D. Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P.-T. Yap, A. Khan (Eds.), Medical Image Computing and Computer ...

    2019

  45. [53]

    X.Guo,Y.Yang,C.Ye,S.Lu,B.Peng,H.Huang,Y.Xiang,T.Ma,Acceleratingdiffusionmodelsviapre-segmentation diffusion sampling for medical image segmentation, in: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), IEEE, 2023, pp. 1–5

    2023

  46. [54]

    S. Mori, R. Hirai, Y. Sakata, Using a deep neural network for four-dimensional ct artifact reduction in image-guided radiotherapy, Physica Medica 65 (2019) 67–75

    2019

  47. [55]

    Isensee, J

    F. Isensee, J. Petersen, A. Klein, D. Zimmerer, P. F. Jaeger, S. Kohl, J. Wasserthal, G. Koehler, T. Norajitra, S. Wirkert, K. H. Maier-Hein, nnu-net: Self-adapting framework for u-net-based medical image segmentation, arXiv preprint arXiv:1809.10486

    arXiv

  48. [56]

    M. Ma, N. Kovalchuk, M. K. Buyyounouski, L. Xing, Y. Yang, Incorporating dosimetric features into the prediction of 3d vmat dose distributions using deep convolutional neural network, Physics in Medicine & Biology 64 (12) (2019) 125017

    2019

  49. [57]

    L. Ma, M. Chen, X. Gu, W. Lu, Deep learning-based inverse mapping for fluence map prediction, Physics in Medicine & Biology 65 (23) (2020) 235035

    2020

  50. [58]

    J. S. Buatti, Integrating standardized head and neck radiotherapy data with deep learning for accurate dose predictions and assisted treatment planning, The University of Texas Health Science Center at San Antonio (2024)

    2024

  51. [59]

    Oktay, J

    O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz, et al., Attention u-net: Learning where to look for the pancreas, arXiv preprint arXiv:1804.03999

    arXiv

  52. [60]

    Paganetti, C

    H. Paganetti, C. Beltran, S. Both, L. Dong, J. Flanz, K. Furutani, C. Grassberger, D. R. Grosshans, A.-C. Knopf, J. A. Langendijk, et al., Roadmap: proton therapy physics and biology, Physics in Medicine & Biology 66 (5) (2021) 05RM01

    2021

  53. [61]

    Fiorino, R

    C. Fiorino, R. Jeraj, C. H. Clark, C. Garibaldi, D. Georg, L. Muren, W. van Elmpt, T. Bortfeld, N. Jornet, Grand challenges for medical physics in radiation oncology, Radiotherapy and Oncology 153 (2020) 7–14

    2020

  54. [62]

    B. Choi, D. K. Shrestha, A. Attia, B. J. Stish, J. Leenstra, J. C. Rwigema, J. Ma, S. U. Lee, J. H. Jeong, J. Kim, J. Kim, C. Beltran, J. C. Park, Deep learning-based dose prediction for prostate cancer with empty bladder protocol: a frameworkforefficientandpersonalizedradioth...

    2025

  55. [63]

    Nemoto, N

    T. Nemoto, N. Futakami, E. Kunieda, M. Yagi, A. Takeda, T. Akiba, E. Mutu, N. Shigematsu, Effects of sample size and data augmentation on u-net-based automatic segmentation of various organs, Radiological Physics and Technology 14 (3) (2021) 318–327, abbreviated as: Radiol Phy...

    2021

  56. [64]

    H.Zhang,L.Ma,A.Lim,J.Ye,L.Lukas,H.Li,N.A.Mayr,E.L.Chang,Dosimetricvalidationforprospectiveclinical trial of grid collimator-based spatially fractionated radiation therapy: dose metrics consistency and heterogeneous pattern reproducibility, International Journal of Radiation On...

    2024

  57. [65]

    J.P.Kirkpatrick,A.J.VanDerKogel,T.E.Schultheiss,Radiationdose–volumeeffectsinthespinalcord,International Journal of Radiation Oncology, Biology, Physics 76 (3) (2010) S42–S49, abbreviated as: Int J Radiat Oncol Biol Phys

    2010

  58. [66]

    Y. R. Lawrence, X. A. Li, I. El Naqa, C. A. Hahn, L. B. Marks, T. E. Merchant, A. P. Dicker, Radiation dose–volume effectsinthebrain, InternationalJournalofRadiationOncology, Biology, Physics76(3)(2010)S20–S27, abbreviated as: Int J Radiat Oncol Biol Phys

    2010

  59. [67]

    CV (Patient-wise mean�SD)

    L. B. Marks, S. M. Bentzen, J. O. Deasy, J. D. Bradley, I. S. Vogelius, I. El Naqa, J. L. Hubbs, J. V. Lebesque, R. D. Timmerman, M. K. Martel, et al., Radiation dose–volume effects in the lung, International Journal of Radiation Oncology, Biology, Physics 76 (3) (2010) S70–S7...

    2010

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.