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Paper Citation Record · LEDGER

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

As of 9 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 6 inbound Pith citation observations for arXiv:2502.08333.

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

pith.paper-citation-record.v1
2502.08333 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:35:36.400568Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:02:50.353056Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:08:43.538424Z

Reference resolution

14 of 14 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved9
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5359a3ea-4ae0-477d-a207-1e405d665bb4 · outbound

This paper cites HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-08T05:35:36.350958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:35:36.350958Z digest=sha256:c3e0dce64412b3465d7111b622fca43eaa381ad5f9b340e463674536e17fa14d

Observation 70cd06d2-6cd3-444a-9c2e-49463e880176 · outbound

This paper cites Attention-based Deep Multiple Instance Learning.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact Attention-based Deep Multiple Instance Learning

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-08T05:35:36.360900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:35:36.360900Z digest=sha256:e957394f6a1ddd995b765359194d5fea746d8f8165bf3b2bf309cb80109cb05a

Observation c44f741a-2ea0-43f9-8a81-b6cc0b175fa3 · outbound

This paper cites Multistain Pretraining for Slide Representation Learning in Pathology.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact Multistain Pretraining for Slide Representation Learning in Pathology

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-08T05:35:36.365966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:35:36.365966Z digest=sha256:8b60eea52f934a29a76eab3a000284b71d65cfd592adbc2f57d8bf34d596b956

Observation 70567e7a-3cae-44e6-a408-51c7c3d99f43 · outbound

This paper cites Graph Foundation Models: Concepts, Opportunities and Challenges.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-08T05:35:36.371179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:35:36.371179Z digest=sha256:2f49d6dfd7bdc3c1a5e551b56e2cb1dec26100a78961a30508f8f025dad59788

Observation d1c7eb9c-4311-4891-8fa3-bda71327122f · outbound

This paper cites Renal digital pathology visual knowledge search platform based on language large model and book knowledge.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact Renal digital pathology visual knowledge search platform based on language large model and book knowledge

Reference 10

Resolution
metadata mismatch
local_arxiv, observed 2026-08-08T05:35:36.536920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:35:36.380442Z digest=sha256:1a1df96f5df8f9c7951dce96048a21294953f4aa0bca3fcb5281429c87db35d1

Observation b3f6df49-e09d-4e20-a3c5-caae57faf200 · outbound

This paper cites DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-08-08T05:35:36.487305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:35:36.395740Z digest=sha256:519ad2ea1f68315072d04349e4517972110c98ec147d884729eefe4ad740878e

Observation 4809f1ac-5fb4-40c3-8d9e-3f663c94bcf8 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact On the Opportunities and Risks of Foundation Models

Reference 97

Resolution
unresolved
no resolver link, observed 2026-08-08T05:35:36.345372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:35:36.345372Z digest=sha256:656e580b43a46b490a72f82b25c4a1e123f257819090ce9c824f3fddd20679af

Observation 94d38365-a242-414d-8bf7-b668a3732c84 · outbound

This paper cites Hibou: A Family of Foundational Vision Transformers for Pathology.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact Hibou: A Family of Foundational Vision Transformers for Pathology

Reference 108

Resolution
unresolved
no resolver link, observed 2026-08-08T05:35:36.385250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:35:36.385250Z digest=sha256:834277bd7a766fede2168cba971758ddd72fb1b377dacc4f2db1bc4c3cb0a648

Observation d97f3e17-6c10-4cfe-90cb-ccf7fac5ff73 · outbound

This paper cites On the Expressive Power of Deep Neural Networks.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact On the Expressive Power of Deep Neural Networks

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-08T05:35:36.390875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:35:36.390875Z digest=sha256:9c7d67973ce3674719008b77bcd473240aacae46996a07fb1454079a77dc79bb

Observation 312a674f-59ff-4222-b20d-bf80156d142d · outbound

This paper cites Annals of Oncology 30 (8): 1232–43.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact Annals of Oncology 30 (8): 1232–43

Reference 2019

Resolution
metadata mismatch
raw_fallback, observed 2026-08-08T05:35:36.624835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:35:36.375938Z digest=sha256:0673e9e9f4e07955633634d31c73dc62d69bfa7701643ac63379a9c4b9d441c8

Observation 570cbbd1-072d-4908-ad9d-12749c886827 · outbound

This paper cites PathM3: A Multimodal Multi-Task Multiple Instance Learning Framework for Whole Slide Image Classification and Captioning.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact PathM3: A Multimodal Multi-Task Multiple Instance Learning Framework for Whole Slide Image Classification and Captioning

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-08T05:35:36.400568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:35:36.400568Z digest=sha256:30c5ba0a31ce9370bed62929c555014edc6691d196cfd4e64dd5daa54b9bebba

Observation 01cf20a6-642a-45f8-9cbe-08b01b70cbb9 · outbound

This paper cites British Journal of Cancer, October.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact British Journal of Cancer, October

Reference 2022

Resolution
verified exact
doi, observed 2026-08-08T05:35:36.452632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:35:36.340765Z digest=sha256:aa6d1d68d134f91bff7e37b9022d4ac97e6ff793e760fe71c46a5131aea62899

Observation a24a0a9f-4ac6-4fea-9d7a-f8c312a689e8 · outbound

This paper cites Evaluating Large Language Models: A Comprehensive Survey.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact Evaluating Large Language Models: A Comprehensive Survey

Reference 2023

Resolution
malformed identifier
no resolver link, observed 2026-08-08T05:35:36.355786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:35:36.355786Z digest=sha256:bb7408552ab63e58769fbc2eddc25413bfa4bf8af0955bbdb7ecc49c139b0a61

Observation b0c66308-8dad-4d1a-ac28-13fb0610e61e · outbound

This paper cites Eureka: Evaluating and Understanding Large Foundation Models.

Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact Eureka: Evaluating and Understanding Large Foundation Models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-08T05:35:36.335153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:35:36.335153Z digest=sha256:ded6dcaf921ae148b8172b9f975c0081ac846d5a5af5a70b040e96064d048427

Pith citing papers

Observation 3354bdcf-0c83-49ec-b35b-41ce9d671033 · inbound

PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology cites this paper.

PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T14:02:50.353056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:02:50.353056Z digest=sha256:8c579fb72ddb639fd0582522b7ed0ac8f53ba828d166b4f9667a1f615041f444

Observation 16c10afe-3cc8-4a5b-9764-690b95160147 · inbound

Towards Robust Foundation Models for Digital Pathology cites this paper.

Towards Robust Foundation Models for Digital Pathology Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T15:11:11.314952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:11:11.314952Z digest=sha256:2e13a065eb915f5080bc556aa3da8df8c77ed79469488896ef10ac4ec71dc59f

Observation 98438f53-56c6-4fb0-bd6c-b8544276bf46 · inbound

Beyond the Failures: Rethinking Foundation Models in Pathology cites this paper.

Beyond the Failures: Rethinking Foundation Models in Pathology Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-18T04:00:50.991847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T03:58:14.423644Z digest=sha256:a557e66763158c383ad562d1f1b8114a7ec58626bcb9c2d731b6adf94ee5e0cc

Observation 21fdac6e-f90c-458d-b01e-83ad7caae3b5 · inbound

Weakly Supervised Multicenter Nancy Index Scoring in Ulcerative Colitis Using Foundation Models cites this paper.

Weakly Supervised Multicenter Nancy Index Scoring in Ulcerative Colitis Using Foundation Models Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:11:18.794428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-08T06:30:54.087355Z digest=sha256:07cd7d7c4dda011035aa56a67f4817a5fbd4474f2f4ed23f2de4cf718ccef234

Observation 17c1692a-e0bd-4a94-8942-3a64900f33f2 · inbound

Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction cites this paper.

Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:46:43.823305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-08T04:21:01.746443Z digest=sha256:a2903406e9bcce7653f1a32699581cd7eb490af56bc1318efd474ca132396aeb

Observation 6101c982-108a-4fb9-8066-19c3ebcf1689 · inbound

Democratizing and accelerating AI-driven pathology research through agentic intelligence cites this paper.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

Reference 11

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T17:08:43.540440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-27T04:34:18.363493Z digest=sha256:6021405ca60d48678ce0e98a6642f905e8ad5224f0b6afe2143ab261ad922c63