Paper Citation Record · LEDGER

Towards Large-Scale Training of Pathology Foundation Models

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2404.15217.

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

pith.paper-citation-record.v1
2404.15217 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:29:52.035841Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:17:22.238405Z

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 d2acac78-d3e9-4fd6-9754-5f909baa94bd · inbound

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts cites this paper.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Towards Large-Scale Training of Pathology Foundation Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:23:21.907783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b020acc5-ab95-4dc4-90a5-ffb96fbc3dbb · inbound

Are the Latent Representations of Foundation Models for Pathology Invariant to Rotation? cites this paper.

Are the Latent Representations of Foundation Models for Pathology Invariant to Rotation? Towards Large-Scale Training of Pathology Foundation Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T14:29:52.035841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d431db5c-2a81-4025-b525-5de27ad84b94 · inbound

Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit\'e, and Aignostics cites this paper.

Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit\'e, and Aignostics Towards Large-Scale Training of Pathology Foundation Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:37.729389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbc77b6c-a8da-40e1-8523-60cfc188c48c · inbound

Reusable specimen-level inference in computational pathology cites this paper.

Reusable specimen-level inference in computational pathology Towards Large-Scale Training of Pathology Foundation Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:08:44.120612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8042bf1-9f94-426f-960d-16be770797c5 · inbound

A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks cites this paper.

A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks Towards Large-Scale Training of Pathology Foundation Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T14:02:28.526530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19a96d6c-8135-425a-a64c-7232c2aa323a · inbound

Current Pathology Foundation Models are unrobust to Medical Center Differences cites this paper.

Current Pathology Foundation Models are unrobust to Medical Center Differences Towards Large-Scale Training of Pathology Foundation Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T00:58:22.806775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dbdfbf2-e6db-4356-9bc0-5745014e71c2 · inbound

Towards Robust Foundation Models for Digital Pathology cites this paper.

Towards Robust Foundation Models for Digital Pathology Towards Large-Scale Training of Pathology Foundation Models

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.811157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55f410b2-2b28-46a1-9c4f-7b8022043edd · inbound

Atlas 2 -- Foundation models for clinical deployment cites this paper.

Atlas 2 -- Foundation models for clinical deployment Towards Large-Scale Training of Pathology Foundation Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T11:50:26.835093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95ceca1e-dd38-4c41-a9ab-75aeaa6f8dec · inbound

Plug-and-Play Logit Fusion for Heterogeneous Pathology Foundation Models cites this paper.

Plug-and-Play Logit Fusion for Heterogeneous Pathology Foundation Models Towards Large-Scale Training of Pathology Foundation Models

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T06:00:59.116727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a1bd2756-ce45-42c8-a7d1-00348d98d785 · inbound

Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction cites this paper.

Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction Towards Large-Scale Training of Pathology Foundation Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:36:24.432125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ee11e879-0d4d-42c6-8503-fdcd0be9921a · inbound

Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning cites this paper.

Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning Towards Large-Scale Training of Pathology Foundation Models

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T07:03:06.333773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2be301ef-75cf-478e-903a-5e4e1c653bd2 · inbound

DaX: Learning General Pathology Representations Across Scales cites this paper.

DaX: Learning General Pathology Representations Across Scales Towards Large-Scale Training of Pathology Foundation Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:17:22.239754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 63f8b509-0223-48ef-af34-5f094f282df8 · inbound

STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation cites this paper.

STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation Towards Large-Scale Training of Pathology Foundation Models

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T16:27:09.503567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 153d3704-ee03-4629-a0a1-260ffab11072 · inbound

GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis cites this paper.

GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis Towards Large-Scale Training of Pathology Foundation Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T15:40:56.127693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.