Paper Citation Record · LEDGER

Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2407.18449.

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

pith.paper-citation-record.v1
2407.18449 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:54:57.597378Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T05:12:05.117643Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8a23b808-3c98-4700-83b7-ea50c1dd0c51 · inbound

Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation cites this paper.

Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T05:54:57.597378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66eeabb7-a71b-4f89-82a0-afebed8733c6 · inbound

Discovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoning cites this paper.

Discovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoning Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:20.032641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cdd0f00-a40c-4745-8725-6f2303ba184c · inbound

Large-scale Self-supervised Video Foundation Model for Intelligent Surgery cites this paper.

Large-scale Self-supervised Video Foundation Model for Intelligent Surgery Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:59.524728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cda2c33-f601-48c1-a6fb-2ee3d9c5a091 · inbound

Segment Anything in Pathology Images with Natural Language cites this paper.

Segment Anything in Pathology Images with Natural Language Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:11.695856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d443be83-406b-49ec-8052-b02a05f65205 · inbound

Emerging AI Approaches for Cancer Spatial Omics cites this paper.

Emerging AI Approaches for Cancer Spatial Omics Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:56.811142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e93aae72-69b8-4de6-877e-fd9d0de65fdb · inbound

From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research cites this paper.

From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 157

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:12:05.119327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cf06b662-94da-48ea-8019-810fbc4f1056 · inbound

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping cites this paper.

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:05.490246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.