Paper Citation Record · LEDGER

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images

As of 17 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2507.13974.

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

pith.paper-citation-record.v1
2507.13974 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:16:22.940920Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

verified exact8
verified fuzzy4
unresolved11
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External citation measurements

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Outbound references

Observation bc3e37b6-5e99-4eea-83b1-b9a63dd7c233 · outbound

This paper cites Bioinformatics35(18), 3461–3467 (02 2019).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Bioinformatics35(18), 3461–3467 (02 2019)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:16:23.218656Z

Source-reported events for the cited work

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Observation 396e1802-7fc6-488a-bcd9-42f14e49aa0d · outbound

This paper cites Nature Reviews Cancer 20, 662–680 (2020).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Nature Reviews Cancer 20, 662–680 (2020)

Reference 2

Resolution: verified exact; doi, observed 2026-08-06T16:16:23.074115Z

Source-reported events for the cited work

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Observation b2e3d6ac-b9eb-43e4-8b83-91bf8002f3bd · outbound

This paper cites Nature Medicine 30, 850–862 (03 2024).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Nature Medicine 30, 850–862 (03 2024)

Reference 3

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Observation 682076e3-f26d-4cd9-9932-3a68fa6dbaad · outbound

This paper cites Masked-attention Mask Transformer for Universal Image Segmentation.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Masked-attention Mask Transformer for Universal Image Segmentation

Reference 4

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Observation 6d3e5d36-6357-49b6-9bcf-066ee781d7ee · outbound

This paper cites Multimodal Whole Slide Foundation Model for Pathology.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Multimodal Whole Slide Foundation Model for Pathology

Reference 5

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Observation 2aa9bccd-bca4-4ee6-832f-2b1f5d3242bd · outbound

This paper cites In: International Conference on Learning Representations (2021), https://openreview .net/forum?id=YicbFdNTTy.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images In: International Conference on Learning Representations (2021), https://openreview .net/forum?id=YicbFdNTTy

Reference 6

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Observation e853eeed-4960-4f1d-b68c-187e41545821 · outbound

This paper cites Phikon-v2, A large and public feature extractor for biomarker prediction.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Phikon-v2, A large and public feature extractor for biomarker prediction

Reference 7

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Observation b8727be0-fc8c-4ca7-aa36-9a57401c0e00 · outbound

This paper cites CA: A Cancer Journal for Clinicians 67 (10 2017).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images CA: A Cancer Journal for Clinicians 67 (10 2017)

Reference 8

Resolution: verified exact; doi, observed 2026-08-06T16:16:23.053670Z

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Observation e0d739e0-9bc6-4858-8c0c-4fb29dceeb8e · outbound

This paper cites Remote Sensing15(5) (2023).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Remote Sensing15(5) (2023)

Reference 9

Resolution: verified exact; doi, observed 2026-08-06T16:16:23.045782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fb1f2ba9-9ae2-43df-afcb-bedfbae8bc41 · outbound

This paper cites Advances In Anatomic Pathology 24, 1 (08 2017).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Advances In Anatomic Pathology 24, 1 (08 2017)

Reference 10

Resolution: verified exact; doi, observed 2026-08-06T16:16:23.038300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ff611f0b-afec-4ded-92df-144886fc11bb · outbound

This paper cites Multimedia Tools and Applications81(28), 41249–41269 (Nov 2022).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Multimedia Tools and Applications81(28), 41249–41269 (Nov 2022)

Reference 11

Resolution: verified exact; doi, observed 2026-08-06T16:16:23.029412Z

Source-reported events for the cited work

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Observation 4496991e-3036-427f-9106-9bf922d46eb0 · outbound

This paper cites CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models

Reference 12

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Observation e2dc778b-9955-4c7a-80c4-34873d5b65c0 · outbound

This paper cites https://doi.org/10.1038/s41592-020-01008-z Leveraging Foundation Models for Tissue Segmentation 15.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images https://doi.org/10.1038/s41592-020-01008-z Leveraging Foundation Models for Tissue Segmentation 15

Reference 13

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Observation 23dda4df-99b4-4a1e-9e41-b251486d6288 · outbound

This paper cites Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T16:16:22.911886Z

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Observation 310652e6-35aa-49d2-86ea-d338f9c111cc · outbound

This paper cites In: Tuba, M., Akashe, S., Joshi, A.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images In: Tuba, M., Akashe, S., Joshi, A

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:16:23.199378Z

Source-reported events for the cited work

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Observation e866834c-cf6c-435a-bcca-70809bd3424f · outbound

This paper cites Communications Medicine 2(1), 120 (sep 2022).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Communications Medicine 2(1), 120 (sep 2022)

Reference 16

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Observation 3bdf07ab-3a28-4cf8-b3be-fc7870e78f6d · outbound

This paper cites an unresolved cited work.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:16:23.190507Z

Source-reported events for the cited work

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Observation b77120b5-25fc-4872-99ef-2ec801e79835 · outbound

This paper cites In: Frangi, A.F., Schnabel, J.A., Da- vatzikos, C., Alberola-López, C., Fichtinger, G.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images In: Frangi, A.F., Schnabel, J.A., Da- vatzikos, C., Alberola-López, C., Fichtinger, G

Reference 18

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Observation fa11923a-72a2-4dfd-b20f-8b1534bd480e · outbound

This paper cites Cell Reports 23(1), 181–193.e7 (2018).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Cell Reports 23(1), 181–193.e7 (2018)

Reference 19

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Observation 1c9cbdac-eb9f-4ea5-932a-0afc8d82ddfc · outbound

This paper cites GigaScience14 (01 2025).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images GigaScience14 (01 2025)

Reference 20

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Observation 45ccd5dd-fc24-4e11-bde4-4c9534c3f08e · outbound

This paper cites Medical Image Analysis 67, 101813 (2021).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Medical Image Analysis 67, 101813 (2021)

Reference 21

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Observation 64e90de0-2da6-438f-b538-66bbe434a421 · outbound

This paper cites EfficientNetV2: Smaller Models and Faster Training.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images EfficientNetV2: Smaller Models and Faster Training

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T16:16:22.932147Z

Source-reported events for the cited work

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Observation 91d147fa-9bce-4f55-844a-fbda2142221b · outbound

This paper cites Modern Pathology31 (12 2017).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Modern Pathology31 (12 2017)

Reference 23

Resolution: verified exact; doi, observed 2026-08-06T16:16:22.981452Z

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Observation cde170e5-2448-4c71-80ab-76e836cc9add · outbound

This paper cites Human Pathology57 (07 2016).

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Human Pathology57 (07 2016)

Reference 24

Resolution: verified exact; doi, observed 2026-08-06T16:16:22.973279Z

Source-reported events for the cited work

correction dated 2017-04-25. Source: crossref record 10.1016/j.humpath.2017.03.001->10.1016/j.humpath.2016.07.008:correction, observed 2026-07-11T03:15:15.93725+00:00. This notice travels one citation hop only.

Observation 0d1b2c86-9f5f-47b4-87a5-17b9df2aa056 · outbound

This paper cites Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 25

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Pith citing papers

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