Paper Citation Record · LEDGER

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts

As of 5 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2410.06723.

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

pith.paper-citation-record.v1
2410.06723 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-23T19:20:36.642563Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T11:50:30.674379Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d2acac78-d3e9-4fd6-9754-5f909baa94bd · outbound

This paper cites Towards Large-Scale Training of Pathology Foundation Models.

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

Reference 1

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Observation 66b89a50-3846-4448-adf4-130c654b211e · outbound

This paper cites Artifi- cial intelligence as the next step towards precision pathology.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Artifi- cial intelligence as the next step towards precision pathology

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:35:48.263007Z

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Observation fcb4c0fb-55df-4d06-b7af-bb3f352c8141 · outbound

This paper cites A systematic pan-cancer study on deep learning-based prediction of multi- omic biomarkers from routine pathology images.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts A systematic pan-cancer study on deep learning-based prediction of multi- omic biomarkers from routine pathology images

Reference 3

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Observation d4de8f6c-74d0-4d28-bda0-e179245a49d5 · outbound

This paper cites Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision

Reference 4

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Observation 358d9809-a434-4a78-8c63-751b1332fddc · outbound

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

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts On the Opportunities and Risks of Foundation Models

Reference 5

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Observation 5f790e3c-b4d7-4846-8938-8cf9e4ab22bb · outbound

This paper cites Artifi- cial intelligence for diagnosis and gleason grading of prostate cancer: the PANDA challenge.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Artifi- cial intelligence for diagnosis and gleason grading of prostate cancer: the PANDA challenge

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:35:48.289522Z

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Observation 531e386d-68c0-4357-be09-16fc0c3cab8e · outbound

This paper cites Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:35:48.312896Z

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Observation e591723f-fbdb-4c9c-aa0b-218f9bf01792 · outbound

This paper cites A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models

Reference 8

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Observation d5dd66f0-595b-40f0-9205-52e24c2f5746 · outbound

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

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Towards a general-purpose foundation model for computational pathology

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:35:48.341929Z

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Observation 4baf5a6a-5502-4f33-ab71-bbf8f2672190 · outbound

This paper cites Artificial intelligence to identify genetic alterations in con- ventional histopathology.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Artificial intelligence to identify genetic alterations in con- ventional histopathology

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:35:48.243577Z

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Observation 4122fce9-9b0a-41e4-b2f8-9bc24342f475 · outbound

This paper cites Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Reference 11

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Observation f4e2b4aa-9742-4821-9ef5-057909045e0f · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts An image is worth 16x16 words: Transformers for image recognition at scale

Reference 12

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Observation 0bc21be4-c1d6-4131-a5c8-dcdb8914aaf6 · outbound

This paper cites Deep learning in cancer pathology: a new generation of clinical biomarkers.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Deep learning in cancer pathology: a new generation of clinical biomarkers

Reference 13

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Observation 2cacaf9b-9186-4a8a-8371-cb584eefb3d2 · outbound

This paper cites An update of the gleason grading system.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts An update of the gleason grading system

Reference 14

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Observation a0c04240-3f44-4080-9ebc-bdfcd621e516 · outbound

This paper cites A contemporary prostate cancer grading system: a validated alternative to the gleason score.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts A contemporary prostate cancer grading system: a validated alternative to the gleason score

Reference 15

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Observation 0f993665-e472-499b-a732-74635e0a1124 · outbound

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

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Scaling self-supervised learning for histopathology with masked image modeling

Reference 16

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Observation e02b2b81-a20b-468d-b7cb-2a8dce42fa52 · outbound

This paper cites The clinician and dataset shift in artificial intelligence.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts The clinician and dataset shift in artificial intelligence

Reference 17

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This paper cites Gustafsson, Martin Danelljan, and Thomas B.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Gustafsson, Martin Danelljan, and Thomas B

Reference 18

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Deep residual learning for image recognition

Reference 19

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This paper cites Benchmarking neu- ral network robustness to common corruptions and perturba- tions.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Benchmarking neu- ral network robustness to common corruptions and perturba- tions

Reference 20

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Observation b3af281a-f963-4fd8-8905-577888c4deb6 · outbound

This paper cites Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning

Reference 21

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This paper cites Attention-based deep multiple instance learning.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Attention-based deep multiple instance learning

Reference 22

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Domain Generalization in Computational Pathology: Survey and Guidelines

Reference 23

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This paper cites End-to-end prognostication in col- orectal cancer by deep learning: a retrospective, multicentre study.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts End-to-end prognostication in col- orectal cancer by deep learning: a retrospective, multicentre study

Reference 24

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This paper cites Wilds: A benchmark of in-the- wild distribution shifts.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Wilds: A benchmark of in-the- wild distribution shifts

Reference 25

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This paper cites Benchmarking weakly- supervised deep learning pipelines for whole slide classifica- tion in computational pathology.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Benchmarking weakly- supervised deep learning pipelines for whole slide classifica- tion in computational pathology

Reference 26

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This paper cites Decoupled weight de- cay regularization.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Decoupled weight de- cay regularization

Reference 27

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Data-efficient and weakly supervised computational pathology on whole- slide images

Reference 28

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts A visual- language foundation model for computational pathology

Reference 29

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Foundation models for generalist medi- cal artificial intelligence

Reference 30

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Hibou: A Family of Foundational Vision Transformers for Pathology

Reference 31

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Benchmarking foundation models as feature extractors for weakly-supervised computational pathology

Reference 32

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This paper cites Generalizable biomarker prediction from can- cer pathology slides with self-supervised deep learning: A retrospective multi-centric study.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Generalizable biomarker prediction from can- cer pathology slides with self-supervised deep learning: A retrospective multi-centric study

Reference 33

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Unresolved cited work

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshmi- narayanan, and Jasper Snoek

Reference 35

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Pedregosa, G

Reference 36

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Dataset shift in ma- chine learning

Reference 37

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Imagenet large 9 scale visual recognition challenge

Reference 38

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts H-optimus-0

Reference 39

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Artificial intelligence in histopathology: enhancing cancer research and clinical on- cology

Reference 40

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This paper cites Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study

Reference 41

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Observation 338be33f-6b05-4a56-888c-3fc8e60f3b57 · outbound

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Prediction of recurrence risk in endometrial cancer with multimodal deep learning

Reference 42

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Observation 9957eea0-ed32-4f79-a92c-3daae6fb9dde · outbound

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts A foundation model for clinical-grade computational pathology and rare cancers detection

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:35:48.274286Z

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Improved breast cancer histological grading using deep learning

Reference 44

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Meneghetti, Omar S

Reference 45

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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts A whole-slide foundation model for digital pathology from real-world data

Reference 46

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This paper cites Coca: Contrastive captioners are image-text foundation models.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Coca: Contrastive captioners are image-text foundation models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:35:48.219771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7d82ad8c-da04-4b74-be8d-3d5322099cf9 · outbound

This paper cites Image BERT pre-training with online tokenizer.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Image BERT pre-training with online tokenizer

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:35:48.302266Z

Source-reported events for the cited work

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Observation 737c8cdf-0487-4962-bc78-5bd1de6eff0d · outbound

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

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 49

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Observation 94e7844e-743c-495c-9b70-0fcd30d9497b · outbound

This paper cites Supplementary Tables Table S1.

Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts Supplementary Tables Table S1

Reference 50

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Pith citing papers

Observation a82a1769-c754-49d5-998f-c30f7cc14c29 · inbound

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

Atlas 2 -- Foundation models for clinical deployment Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts

Reference 29

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