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Paper Citation Record · LEDGER

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data?

As of 15 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2412.15967.

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

pith.paper-citation-record.v1
2412.15967 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:58:46.382320Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

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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  • verified fuzzy15
  • unresolved10
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e79ef302-9b53-4e33-83bd-01571bfdea3f · outbound

This paper cites 2021 IEEE/CVF International Conference on Computer Vision (ICCV) pp.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? 2021 IEEE/CVF International Conference on Computer Vision (ICCV) pp

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 8ec35fe4-753c-4700-9105-890c32fd9f75 · outbound

This paper cites an unresolved cited work.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? Unresolved cited work

Reference 2

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7e46c5c0-cc61-4c34-88f0-d1b87ad5118c · outbound

This paper cites Deep Clustering for Unsupervised Learning of Visual Features.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? Deep Clustering for Unsupervised Learning of Visual Features

Reference 3

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f2655c06-4a3a-4f25-a4b0-f5cc39e64ae0 · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? A Simple Framework for Contrastive Learning of Visual Representations

Reference 4

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e14dd822-ae4a-4736-9681-0a1babfcb303 · outbound

This paper cites European Radiology31, 1812 – 1818 (2020).

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? European Radiology31, 1812 – 1818 (2020)

Reference 5

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation d7c31c7a-7857-4995-986b-a836a25e73f1 · outbound

This paper cites Journal of Digital Imaging34, 66 – 74 (2020).

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? Journal of Digital Imaging34, 66 – 74 (2020)

Reference 6

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 0dcf0e0f-2510-4bf2-892f-5f46efd603b0 · outbound

This paper cites an unresolved cited work.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? Unresolved cited work

Reference 7

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation b79fedf9-2001-4a28-aba4-7f430574119f · outbound

This paper cites Bootstrap your own latent: A new approach to self-supervised Learning.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? Bootstrap your own latent: A new approach to self-supervised Learning

Reference 8

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a3b8fef1-9afc-46ba-80c6-bf651cf0c84e · outbound

This paper cites In: SPIE Medical Imaging (2002).

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? In: SPIE Medical Imaging (2002)

Reference 9

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation d54f06d9-6a6b-4b99-bcac-a0b2cee6080d · outbound

This paper cites 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp

Reference 10

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation b7350cc5-4315-4ad7-b86b-320a1957a3f2 · outbound

This paper cites European Radiology33, 1537 – 1544 (2022).

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? European Radiology33, 1537 – 1544 (2022)

Reference 11

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation f4828b67-c9f9-444f-becb-68e36740c69e · outbound

This paper cites In: International Conference on Machine Learning (2015).

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? In: International Conference on Machine Learning (2015)

Reference 12

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation c5ea4a9d-6cab-4ff2-8357-a1a20a85083a · outbound

This paper cites European Radiology32, 8769 – 8776 (2022).

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? European Radiology32, 8769 – 8776 (2022)

Reference 13

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation d83aad47-89d9-4d31-ad9c-7ef3bdf3d2c1 · outbound

This paper cites In: Engelhardt, S., Oksuz, I., Zhu, D., Yuan, Y., Mukhopadhyay, A., Heller, N., Huang, S.X., Nguyen, H., Sznitman, R., Xue, Y.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? In: Engelhardt, S., Oksuz, I., Zhu, D., Yuan, Y., Mukhopadhyay, A., Heller, N., Huang, S.X., Nguyen, H., Sznitman, R., Xue, Y

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 3a773d4c-8718-4264-8db7-ffe8ef710157 · outbound

This paper cites In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H

Reference 15

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 03609a14-e359-4e92-8d74-b1221a0c1921 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? Adam: A Method for Stochastic Optimization

Reference 16

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 24e6a14b-b208-4bf3-be78-06926851f597 · outbound

This paper cites Captum: A unified and generic model interpretability library for PyTorch.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? Captum: A unified and generic model interpretability library for PyTorch

Reference 17

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0431b1e2-0871-4106-990b-165ddafac50b · outbound

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Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? Radiology p

Reference 18

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 45d860a8-f5ff-44e9-9c8a-d173c456040f · outbound

This paper cites arXiv: Learning (2016).

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? arXiv: Learning (2016)

Reference 19

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f973c64d-d3c4-4191-a366-fecfe94c3ee7 · outbound

This paper cites Journal of Machine Learning Research 9, 2579–2605 (2008).

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? Journal of Machine Learning Research 9, 2579–2605 (2008)

Reference 20

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 2c200d8e-eee7-4b2a-9884-bdd7f029f3d6 · outbound

This paper cites In: 5th Berkeley Symp.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? In: 5th Berkeley Symp

Reference 21

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation b5d401a8-b4d4-4d6d-9941-f9031a04bf18 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 22

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 93b64cc6-f0e5-4fd5-8954-9b245ee0ad3b · outbound

This paper cites In- ternational Journal of Computer Vision128, 336–359 (2016).

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? In- ternational Journal of Computer Vision128, 336–359 (2016)

Reference 23

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 97a8bb6c-cad5-486f-a523-d5be66b83a8d · outbound

This paper cites The American journal of tropical medicine and hygiene88 4, 608–13 (2013).

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? The American journal of tropical medicine and hygiene88 4, 608–13 (2013)

Reference 24

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 1dac47be-b1ff-452f-9e4f-d75b3d200254 · outbound

This paper cites an unresolved cited work.

Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data? Unresolved cited work

Reference 25

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Pith citing papers

No inbound Pith citation observations are available.