Paper Citation Record · LEDGER

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization

As of 19 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2507.01539.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.01539 v1

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measured 39 of 39 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation 14cbf278-bd9e-47dc-934a-7c7ea3b0e1da · outbound

This paper cites Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.Nat Com- mun, 5:4006, 6 2014.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.Nat Com- mun, 5:4006, 6 2014

Reference 1

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Observation 4c7a1f7c-3469-41d6-8080-0e90b14b7a03 · outbound

This paper cites Prepnet: A convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysis.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Prepnet: A convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysis

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:14.486079Z

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Observation a3422e20-daef-4e1c-b1f8-8f6ac6e3454d · outbound

This paper cites Learning cross-protocol radiomics and deep feature standardization from CT im- ages of texture phantoms.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Learning cross-protocol radiomics and deep feature standardization from CT im- ages of texture phantoms

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:14.326824Z

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Observation 0f670e23-2f51-46f7-8c3a-31f75a44d737 · outbound

This paper cites Neu- ral network training for cross-protocol radiomic feature standardization in computed tomography.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Neu- ral network training for cross-protocol radiomic feature standardization in computed tomography

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:14.168617Z

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Observation 5dd60d5d-0e3e-4344-959e-8e34652cb559 · outbound

This paper cites Pilot study for the assessment of the best radiomic features for bosniak cyst classification using phantom and radiologist inter- observer selection.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Pilot study for the assessment of the best radiomic features for bosniak cyst classification using phantom and radiologist inter- observer selection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:13.994352Z

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Observation 1736f1bc-6dc2-4edf-8f46-e533c1332015 · outbound

This paper cites 3D-printed iodine-ink CT phan- tom for radiomics feature extraction-advantages and challenges.Medical Physics, 50(9):5682–5697, 2023.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization 3D-printed iodine-ink CT phan- tom for radiomics feature extraction-advantages and challenges.Medical Physics, 50(9):5682–5697, 2023

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:13.765590Z

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Observation ed1f22db-ab16-4158-8b9e-f88f6b661194 · outbound

This paper cites On various intraclass correlation reliability coefficients.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization On various intraclass correlation reliability coefficients

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:13.556951Z

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Observation 473d66fb-325a-46f6-ba7e-0e7c7c144ac3 · outbound

This paper cites Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:56:11.200693Z

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Observation 4a110508-c1a0-41be-a8a9-d143b4474452 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:13.375311Z

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Observation 2fcf10de-9b94-4f6a-a79a-65b4d3023085 · outbound

This paper cites An annotated test-retest collection of prostate multiparametric MRI.Scientific Data 2018 5:1, 5:1–13, 12 2018.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization An annotated test-retest collection of prostate multiparametric MRI.Scientific Data 2018 5:1, 5:1–13, 12 2018

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:13.163336Z

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Observation d96ed1a3-b6db-40d1-a49c-9774468089ab · outbound

This paper cites Radiomics: images are more than pictures, they are data.Radiology, 278(2):563–577, 2016.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Radiomics: images are more than pictures, they are data.Radiology, 278(2):563–577, 2016

Reference 11

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Observation 51ca7674-6229-41c2-bdd6-adbb4c64debc · outbound

This paper cites Eval- uation of domain generalization and adaptation on improving model ro- bustness to temporal dataset shift in clinical medicine.Scientific reports, 12(1):2726, 2022.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Eval- uation of domain generalization and adaptation on improving model ro- bustness to temporal dataset shift in clinical medicine.Scientific reports, 12(1):2726, 2022

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:12.790400Z

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Observation 34e75bf7-776d-42c0-bbe8-77c47ef15233 · outbound

This paper cites The many faces of robustness: A critical analysis of out-of- distribution generalization.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization The many faces of robustness: A critical analysis of out-of- distribution generalization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:12.654801Z

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Observation 28b84e14-8f5f-4193-9b70-b82ad6dd76ed · outbound

This paper cites Gaussian Error Linear Units (GELUs).

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Gaussian Error Linear Units (GELUs)

Reference 14

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Observation 585c9c3d-7799-49a8-ab28-353487fa02c2 · outbound

This paper cites Phantom-based radiomics feature test–retest stability analysis on photon-counting detector CT.Eu- ropean Radiology, 33(7):4905–4914, 2023.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Phantom-based radiomics feature test–retest stability analysis on photon-counting detector CT.Eu- ropean Radiology, 33(7):4905–4914, 2023

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:12.511016Z

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Observation be26c9f3-c173-4304-bcfa-77a2ba6c6603 · outbound

This paper cites Radiopaque three- dimensional printing: a method to create realistic CT phantoms.Radiology, 282(2):569–575, 2017.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Radiopaque three- dimensional printing: a method to create realistic CT phantoms.Radiology, 282(2):569–575, 2017

Reference 16

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A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Unresolved cited work

Reference 17

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Observation 291bbb4e-6bec-4ed6-842b-57e718c8d231 · outbound

This paper cites Obmann, André Anjos, Henning Müller, and Adrien Depeursinge.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Obmann, André Anjos, Henning Müller, and Adrien Depeursinge

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:12.089439Z

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Observation 27062fb8-ba6b-4cab-acb5-157646c5f02b · outbound

This paper cites Radiomics: the bridge between medical imaging and personalized medicine.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Radiomics: the bridge between medical imaging and personalized medicine

Reference 19

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Observation d21dd31e-1d1c-4b98-8ba4-51edfb7cf073 · outbound

This paper cites Gradient- based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 1998.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Gradient- based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 1998

Reference 20

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Observation 6d147852-d08e-44ce-820a-a83fa8175154 · outbound

This paper cites Style transfer using generative adversarial networks for multi-site MRI harmonization.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Style transfer using generative adversarial networks for multi-site MRI harmonization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.720958Z

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Observation ca8071e9-1266-4218-b183-2462804cc2a5 · outbound

This paper cites Learning disentangled representations in the imag- ing domain.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Learning disentangled representations in the imag- ing domain

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.576476Z

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Observation a4b6e025-9924-45bc-b6b9-a0e11e8ac9fc · outbound

This paper cites Measuring computed tomography scanner variability of ra- diomics features.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Measuring computed tomography scanner variability of ra- diomics features

Reference 23

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Observation 4accdc93-7849-4f4d-97f4-a2185d993b09 · outbound

This paper cites Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods

Reference 24

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Observation 008f99fb-51e3-4ce9-850b-71ab62345a21 · outbound

This paper cites Generative adversarial networks 15 improve the reproducibility and discriminative power of radiomic features.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Generative adversarial networks 15 improve the reproducibility and discriminative power of radiomic features

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.472017Z

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Observation 26388d0a-4a29-4745-b4bd-72a72b64d4b2 · outbound

This paper cites Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study

Reference 26

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Observation b3ca20d1-9ef9-49cc-a037-f8b484e00820 · outbound

This paper cites Exploring generalization in deep learning.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Exploring generalization in deep learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T20:56:11.080136Z

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Observation e0eed12c-34d8-49c0-8159-cf8f3ed63cf1 · outbound

This paper cites A guide to combat harmonization of imaging biomarkers in multi- center studies.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization A guide to combat harmonization of imaging biomarkers in multi- center studies

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.408187Z

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Observation 3ce17745-4fbb-4622-a078-c3f195aa444c · outbound

This paper cites Radiomics: the facts and the challenges of image analysis.European radiology experi- mental, 2:1–8, 2018.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Radiomics: the facts and the challenges of image analysis.European radiology experi- mental, 2:1–8, 2018

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.384475Z

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Observation 63e56278-9320-4f2d-968e-c99165bd6fef · outbound

This paper cites Intraclass correlations: uses in assessing rater reliability.Psychological bulletin, 86(2):420, 1979.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Intraclass correlations: uses in assessing rater reliability.Psychological bulletin, 86(2):420, 1979

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.363118Z

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Observation b1665a81-5a17-4eb5-aed7-a4411866bf24 · outbound

This paper cites Alzheimer’s disease classification accuracy is improved by MRI harmonization based on attention-guided generative adversarial networks.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Alzheimer’s disease classification accuracy is improved by MRI harmonization based on attention-guided generative adversarial networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.347420Z

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Observation bb792cbe-80e3-4199-b6fa-97ba7a56a10d · outbound

This paper cites Roth, Bennett Land- man, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Roth, Bennett Land- man, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.328912Z

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Observation d05a45db-bcfc-48a8-bb8c-980109f46518 · outbound

This paper cites Unsupervised MRI homogenization: ap- plication to pediatric anterior visual pathway segmentation.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Unsupervised MRI homogenization: ap- plication to pediatric anterior visual pathway segmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.309833Z

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Observation a61c77a1-e3df-4e58-a4ba-71a8abb9eddc · outbound

This paper cites Visualizing non-metric sim- ilarities in multiple maps.Machine Learning, 87(1):33–55, 2012.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Visualizing non-metric sim- ilarities in multiple maps.Machine Learning, 87(1):33–55, 2012

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.293270Z

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Observation bef46b3a-5507-4244-913a-40720ee7a6e7 · outbound

This paper cites Compu- tational radiomics system to decode the radiographic phenotype.Cancer Research, 77:e104–e107, 2017.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Compu- tational radiomics system to decode the radiographic phenotype.Cancer Research, 77:e104–e107, 2017

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.277739Z

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Observation 8fe16ec1-2b6d-4bb0-8c67-9fb10d988331 · outbound

This paper cites Radiomic feature robustness evaluations in ultrasound imaging.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Radiomic feature robustness evaluations in ultrasound imaging

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.261181Z

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Observation a0f4d828-4c83-4021-b4d6-01f5e6435711 · outbound

This paper cites Contrastive cross-site learning with redesigned net for COVID-19 CT classification.IEEE Journal of Biomed- ical and Health Informatics, 24(10):2806–2813, 2020.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Contrastive cross-site learning with redesigned net for COVID-19 CT classification.IEEE Journal of Biomed- ical and Health Informatics, 24(10):2806–2813, 2020

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:56:11.245891Z

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Observation 49d02f33-65f6-4b3b-965c-cfd95fcec85a · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Image quality assessment: from error visibility to structural similarity

Reference 38

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Observation abdc3efc-ffd0-4b2e-8ff8-213b2d5d924e · outbound

This paper cites Radiomic feature repeatability and its impact on prognostic model generalizability: A multi-institutional study on nasopha- ryngeal carcinoma patients.

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization Radiomic feature repeatability and its impact on prognostic model generalizability: A multi-institutional study on nasopha- ryngeal carcinoma patients

Reference 39

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Pith citing papers

Observation 9c3e096b-e2de-4d47-8fa4-8b56e769d8ef · inbound

Distribution Steering via Sliced Optimal Transport Control cites this paper.

Distribution Steering via Sliced Optimal Transport Control A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:35:24.982908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.