Paper Citation Record · LEDGER

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification

As of 18 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2412.06365.

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

pith.paper-citation-record.v1
2412.06365 v2

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:48:23.083312Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

12 of 12 outbound references displayed

verified exact0
verified fuzzy6
unresolved5
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f6fe5020-6264-4ca8-91ec-163112ec1914 · outbound

This paper cites Do- main generalization across tumor types, laboratories, and species—Insights from the 2022 editionoftheMitosisDomainGeneralizationChallenge.MedicalImageAnalysis94(2024), p.

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification Do- main generalization across tumor types, laboratories, and species—Insights from the 2022 editionoftheMitosisDomainGeneralizationChallenge.MedicalImageAnalysis94(2024), p

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:48:23.635838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 14d47bce-64f6-4877-ad8b-79bc0e380d09 · outbound

This paper cites Scientific Reports 14.1 (2024), p.

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification Scientific Reports 14.1 (2024), p

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:48:23.620844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6104e989-c13e-4459-88e3-521812f7d182 · outbound

This paper cites Towards a general- purpose foundation model for computational pathology.

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification Towards a general- purpose foundation model for computational pathology

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:48:23.552354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c478e8b7-c7a8-4665-aa26-3fe652e11900 · outbound

This paper cites A foundation model for clinical-grade computational pathology and rare cancers detection.

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification A foundation model for clinical-grade computational pathology and rare cancers detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:48:23.420128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 139e65cb-9a06-4bfd-94f4-6501f5e97dfb · outbound

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

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T19:48:23.046400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3698f647-adda-4bb1-8e9d-9e6009194ea5 · outbound

This paper cites Scaling self-supervised learning for histopathology with masked image modeling.

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification Scaling self-supervised learning for histopathology with masked image modeling

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:48:23.403548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 61c6162f-b0b0-4811-850a-484dd649b173 · outbound

This paper cites an unresolved cited work.

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:48:23.388637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 72be0fd9-fee0-4157-ac9e-64fc18c1568a · outbound

This paper cites A whole-slide foundation model for digital pathology from real-world data.

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification A whole-slide foundation model for digital pathology from real-world data

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:48:23.372060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 388d0bbf-d8a0-4831-8ae6-aae0da9dd015 · outbound

This paper cites Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region.

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region

Reference 9

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T19:48:23.355019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 14be8508-69fb-4154-90ec-14844cf39abd · outbound

This paper cites an unresolved cited work.

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:48:23.242461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0ffe1e7b-4b7f-4abe-9f5e-2a2334f9434b · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification DINOv2: Learning Robust Visual Features without Supervision

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T19:48:23.077141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 510e32af-426d-4b1f-95ee-d69a285bad44 · outbound

This paper cites Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates.

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T19:48:23.083312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.