Paper Citation Record · LEDGER

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2412.06610.

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

pith.paper-citation-record.v1
2412.06610 v1

Coverage vector

measured 32 of 32 reference resolution

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measured 32 of 32 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

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External citation measurements

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Outbound references

Observation 987b96ec-5baa-4c60-9521-be2249eae88f · outbound

This paper cites In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A

Reference 1

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Observation aac6bc04-1854-4ef3-b4cd-8869e1bb5b07 · outbound

This paper cites In: 3D head and neck tumor segmentation in PET/CT challenge, pp.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: 3D head and neck tumor segmentation in PET/CT challenge, pp

Reference 2

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Observation 451092b3-aed0-463c-9868-1fb76fe40811 · outbound

This paper cites In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Reference 3

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Observation 6e744be4-cacc-4393-8bbf-12dd188de27b · outbound

This paper cites In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1

Reference 4

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Observation 9a490d2d-a13c-42f2-a697-f123a07d9145 · outbound

This paper cites Bio- engineering 10(2) (2023).

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble Bio- engineering 10(2) (2023)

Reference 5

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Observation fbeaf8e8-9d29-405b-bb4f-40734f337c9b · outbound

This paper cites Radiation Oncology15, 1–9 (2020).

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble Radiation Oncology15, 1–9 (2020)

Reference 6

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Observation 03bdcba9-b364-49f8-8e36-01f5d86f240d · outbound

This paper cites In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge, pp.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge, pp

Reference 7

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Observation 8710618e-5ce8-4622-9920-66ba25c6cef5 · outbound

This paper cites In: Medical Image ComputingandComputer-AssistedIntervention–MICCAI2016:19thInternational Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: Medical Image ComputingandComputer-AssistedIntervention–MICCAI2016:19thInternational Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19

Reference 8

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Observation 6cf1ec37-220e-4e06-bf83-6fba71f02fe3 · outbound

This paper cites AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

Reference 9

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Observation 14d56e47-523c-4168-93f3-44d4dc077c62 · outbound

This paper cites In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Con- junction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Con- junction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1

Reference 10

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Observation f83e7928-5bf1-4866-bd1e-8d2370780ec8 · outbound

This paper cites In: International MICCAI brainlesion workshop.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: International MICCAI brainlesion workshop

Reference 11

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Observation 6ba1053a-75f9-4027-8cce-1d55f37e897d · outbound

This paper cites Contrast media & molecular imaging2018(1), 8923028 (2018).

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble Contrast media & molecular imaging2018(1), 8923028 (2018)

Reference 12

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Observation 1dcf55db-19c0-4a72-9953-35bfff0ba222 · outbound

This paper cites Medical physics44(2), 547–557 (2017).

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble Medical physics44(2), 547–557 (2017)

Reference 13

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Observation 64883e9c-4ff0-4539-9c94-a98bb934dd9b · outbound

This paper cites nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

Reference 14

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Observation 903fb60a-3bdd-4fcf-a9cc-146e05e8d608 · outbound

This paper cites nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation

Reference 15

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Observation bf9e6290-89d9-44df-ae4a-ea3abccdc53d · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

Reference 16

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Observation aa1bad81-040b-4016-90ae-f12d1a9f045c · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 17

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Observation 05dfbada-5c46-412e-9c4e-1718258207bc · outbound

This paper cites Nature Communications15(1), 654 (2024).

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble Nature Communications15(1), 654 (2024)

Reference 18

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Observation e6c89ef9-7026-430a-90f6-7a2bad775977 · outbound

This paper cites In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Reference 19

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Observation 22f7f03a-1f65-4d43-a48e-491121e7b1be · outbound

This paper cites Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancers.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancers

Reference 20

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Observation 0e2d4df2-0b2b-4350-98f4-2b5aa0959318 · outbound

This paper cites https:// doi.org/10.5281/zenodo.14193311.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble https:// doi.org/10.5281/zenodo.14193311

Reference 21

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Observation c09d57a2-6a46-41ba-a1bd-965006972a50 · outbound

This paper cites https://doi.org/10.5281/zenodo.12542217.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble https://doi.org/10.5281/zenodo.12542217

Reference 22

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Observation a08946e4-8c82-4efa-8aec-1c3d7b9e8179 · outbound

This paper cites In: Andrearczyk, V., Oreiller, V., Hatt, M., De- peursinge, A.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: Andrearczyk, V., Oreiller, V., Hatt, M., De- peursinge, A

Reference 23

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Observation ecdf286b-4de0-479a-944c-1dd3beb95c9c · outbound

This paper cites Physics and Imaging in Radia- tion Oncology 32, 100655 (2024).

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble Physics and Imaging in Radia- tion Oncology 32, 100655 (2024)

Reference 24

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Observation 2d716a75-fdc8-4216-8191-82d37bb61e1a · outbound

This paper cites Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images

Reference 25

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T19:33:05.294539Z

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Observation 16c57470-d01b-4357-b7b5-af371399ff21 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 26

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Observation c311d396-a792-4ba4-97b7-8dcf2b4190d7 · outbound

This paper cites CA: a cancer journal for clinicians 71(3), 209–249 (2021).

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble CA: a cancer journal for clinicians 71(3), 209–249 (2021)

Reference 27

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Observation 6a32847e-2798-4191-a056-511ed1648157 · outbound

This paper cites AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models

Reference 28

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This paper cites Radiotherapy MRI-based HNC segmentation by 15-fold cross-validation ensemble 13 and Oncology 112(3), 317–320 (Sep 2014).https://doi.org/10.1016/j.radonc.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble Radiotherapy MRI-based HNC segmentation by 15-fold cross-validation ensemble 13 and Oncology 112(3), 317–320 (Sep 2014).https://doi.org/10.1016/j.radonc

Reference 29

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Observation d5c187ad-dbb6-4b5f-a761-d263faa9ce80 · outbound

This paper cites Clinical and Translational Radi- ationOncology 32,6–14(2022).

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble Clinical and Translational Radi- ationOncology 32,6–14(2022)

Reference 30

Resolution: verified exact; doi, observed 2026-08-11T19:33:05.227281Z

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Observation 7cffb491-cdba-4726-92e2-fa76ef847efa · outbound

This paper cites In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge, pp.

MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge, pp

Reference 31

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Observation d05cb0d9-32ee-40da-94ab-6ac7289154ef · outbound

This paper cites In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1.

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:33:05.485342Z

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Pith citing papers

No inbound Pith citation observations are available.