Paper Citation Record · LEDGER

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI

As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2506.19590.

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

pith.paper-citation-record.v1
2506.19590 v1

Coverage vector

measured 25 of 25 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-06T23:12:46.327990Z

measured 25 of 25 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

25 of 25 outbound references displayed

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External citation measurements

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Outbound references

Observation c260fc5e-d699-4110-af43-5d3378523049 · outbound

This paper cites epub 2023 Apr.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI epub 2023 Apr

Reference 9

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Observation 19a64877-32f6-483d-9433-5419e45cfc3f · outbound

This paper cites MONAI: An open-source framework for deep learning in healthcare.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI MONAI: An open-source framework for deep learning in healthcare

Reference 11

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Observation a17058c4-9439-463e-a31e-3c8d4b25d666 · outbound

This paper cites Korean Journal of Radiology 25, 363–373.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Korean Journal of Radiology 25, 363–373

Reference 12

Resolution: verified exact; raw_fallback, observed 2026-08-06T23:12:47.244388Z

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Observation e92cd66e-7b89-4115-be00-9b17b62631ed · outbound

This paper cites How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?

Reference 13

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Observation 2642b54e-1668-450d-ad76-5542459d29e7 · outbound

This paper cites Frontiers in Oncology 11, 772530.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Frontiers in Oncology 11, 772530

Reference 14

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Observation 6aed0036-cc63-495e-9de7-62fdc2918eb4 · outbound

This paper cites Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

Reference 17

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Observation a46fac4c-8029-4064-9c69-b5f38fb8c15f · outbound

This paper cites Journal of Magnetic Resonance Imaging 55, 653–680.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Journal of Magnetic Resonance Imaging 55, 653–680

Reference 19

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Observation 21fa91b3-9ccd-40f8-aba8-aee1171ca9b2 · outbound

This paper cites An OpenMind for 3D medical vision self-supervised learning.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI An OpenMind for 3D medical vision self-supervised learning

Reference 20

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Observation 2bc7a685-749a-404f-9f9b-7db6c74c425e · outbound

This paper cites TotalSegmentator: robust segmentation of 104 anatomical structures in CT images.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI TotalSegmentator: robust segmentation of 104 anatomical structures in CT images

Reference 21

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Observation e8efcd0c-aeef-410d-a45f-52641dfae89d · outbound

This paper cites Medical Im- age Analysis 67, 101840.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Medical Im- age Analysis 67, 101840

Reference 25

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Observation fa0a4fd3-f4b9-458f-b3bc-91709694766d · outbound

This paper cites pMID: 36194301; PMCID: PMC9525241.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI pMID: 36194301; PMCID: PMC9525241

Reference 159

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Observation 7ba45dee-9a00-4f72-8da9-acf0d5dff6ac · outbound

This paper cites Biometrics Bulletin 1, 80–83.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Biometrics Bulletin 1, 80–83

Reference 1945

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This paper cites Springer Netherlands, Dor- drecht.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Springer Netherlands, Dor- drecht

Reference 1994

Resolution: verified exact; doi, observed 2026-08-06T23:12:46.394342Z

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Observation 3fb22f47-a655-40da-af7b-7f67f5807d25 · outbound

This paper cites IEEE Transactions on Medical Imaging 29, 1310–1320.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI IEEE Transactions on Medical Imaging 29, 1310–1320

Reference 2010

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Observation a243616d-8878-4fc3-99b9-1e6686a1a5df · outbound

This paper cites Magnetic Resonance Imaging 30, 1323–1341.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Magnetic Resonance Imaging 30, 1323–1341

Reference 2012

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Observation 3ac8a5af-839c-47be-823b-f397e9fe5704 · outbound

This paper cites Radiology 275, 155–166.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Radiology 275, 155–166

Reference 2015

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Observation 3c46a422-1c4c-41d3-901f-28d2869f6be7 · outbound

This paper cites 3342–3345.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI 3342–3345

Reference 2016

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Observation 65df0e7c-2b5c-46fe-bb6f-3259298f3ee4 · outbound

This paper cites URL: https://doi.org/10.5281/zenodo.1169361, doi:10.5281/zenodo.1169361.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI URL: https://doi.org/10.5281/zenodo.1169361, doi:10.5281/zenodo.1169361

Reference 2018

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Observation e4a21e27-2d5a-4488-868f-a58041faf381 · outbound

This paper cites Magnetic Resonance in Medicine 82, 1872–1884.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Magnetic Resonance in Medicine 82, 1872–1884

Reference 2019

Resolution: verified exact; doi, observed 2026-08-06T23:12:46.508874Z

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Observation 0f228dae-595a-44d3-b258-57fd73b9ffaf · outbound

This paper cites (Eds.), Medical Imaging 2020: Computer-Aided Diagnosis, SPIE.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI (Eds.), Medical Imaging 2020: Computer-Aided Diagnosis, SPIE

Reference 2020

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Observation 1cbcb047-d886-483f-a549-fffd0722d0fe · outbound

This paper cites Physiological Reviews 101, 797–855.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Physiological Reviews 101, 797–855

Reference 2021

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Observation dfc5afd6-330f-4ac1-bf3c-41c7c46ef457 · outbound

This paper cites Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 2022

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Observation a5d1e372-75c6-4f7d-a481-11d5941abb68 · outbound

This paper cites Medical Image Analysis 89, 102879.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Medical Image Analysis 89, 102879

Reference 2023

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Observation 08a291ec-4efe-4ae2-9e24-199225c34afd · outbound

This paper cites SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI

Reference 2024

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Observation 246396c5-2618-432a-aaae-26fa6476aada · outbound

This paper cites Armstrong, R.A.,.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Armstrong, R.A.,

Reference 4128

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Pith citing papers

No inbound Pith citation observations are available.