Paper Citation Record · LEDGER

Towards Robust Foundation Models for Digital Pathology

As of 10 August 2026, this Paper Citation Record lists 100 of 111 outbound references and 7 inbound Pith citation observations for arXiv:2507.17845.

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

pith.paper-citation-record.v1
2507.17845 v1

Coverage vector

measured 100 of 111 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:19:58.290831Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:17:22.235915Z

Reference resolution

100 of 111 outbound references displayed

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Outbound references

Observation 3db4c53e-266a-4b2c-8811-c6c3349b9671 · outbound

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

Towards Robust Foundation Models for Digital Pathology On the Opportunities and Risks of Foundation Models

Reference 1

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Observation c44de045-e047-4110-9c39-31a5df8fd235 · outbound

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

Towards Robust Foundation Models for Digital Pathology Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision

Reference 2

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Observation a8206200-70d3-4af2-85c8-b2d1e2f885fa · outbound

This paper cites A comprehensive survey of foundation models in medicine.

Towards Robust Foundation Models for Digital Pathology A comprehensive survey of foundation models in medicine

Reference 3

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

This paper cites Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact.

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

Reference 4

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Observation b4e13bb9-0b1f-4cf9-ad8d-47233bb83ebc · outbound

This paper cites Self supervised contrastive learning for digital histopathology.

Towards Robust Foundation Models for Digital Pathology Self supervised contrastive learning for digital histopathology

Reference 5

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Observation 9d09a9e9-1e56-49e5-9c53-1e7e76af3aa3 · outbound

This paper cites A clinical benchmark of public self-supervised pathology foundation models.

Towards Robust Foundation Models for Digital Pathology A clinical benchmark of public self-supervised pathology foundation models

Reference 6

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Observation 4b95d3bd-4841-476d-aea8-f9c2a798737b · outbound

This paper cites Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning.

Towards Robust Foundation Models for Digital Pathology Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning

Reference 7

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Observation 58724836-754f-4155-a627-8efcfba27473 · outbound

This paper cites A multimodal biomedical foundation model trained from fifteen million image–text pairs.

Towards Robust Foundation Models for Digital Pathology A multimodal biomedical foundation model trained from fifteen million image–text pairs

Reference 8

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Observation 7778800f-e490-40a5-aa4b-ffb0303946d6 · outbound

This paper cites BioBERT: a pre-trained biomedical language representation model for biomedical text mining.

Towards Robust Foundation Models for Digital Pathology BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Reference 9

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Observation 1dccff0c-5e96-43e6-ac00-ac8a21a9d614 · outbound

This paper cites Domain-specific language model pretraining for biomedical natural language processing.

Towards Robust Foundation Models for Digital Pathology Domain-specific language model pretraining for biomedical natural language processing

Reference 10

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Observation 88fa851b-722b-45ca-b547-ca0028160f70 · outbound

This paper cites Transfer learning enables predictions in network biology.

Towards Robust Foundation Models for Digital Pathology Transfer learning enables predictions in network biology

Reference 11

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Observation fc98f08d-7435-4c8e-b169-72561e80f021 · outbound

This paper cites A foundation model of transcription across human cell types.

Towards Robust Foundation Models for Digital Pathology A foundation model of transcription across human cell types

Reference 12

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Observation a12d37ad-d062-4db5-8fc8-61b1b9cf9fa9 · outbound

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

Towards Robust Foundation Models for Digital Pathology Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 13

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Observation d0f93a3d-da77-4147-8f34-f3e97e4d8c83 · outbound

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

Towards Robust Foundation Models for Digital Pathology Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit\'e, and Aignostics

Reference 14

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Observation fb5f98c4-5a28-4e79-b753-2b1cbd980984 · outbound

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

Towards Robust Foundation Models for Digital Pathology Towards a general-purpose foundation model for computational pathology

Reference 15

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Observation 3a118d54-e31d-444b-98be-6471c4d00cbf · outbound

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

Towards Robust Foundation Models for Digital Pathology A foundation model for clinical-grade computational pathology and rare cancers detection

Reference 16

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Observation 3c71b864-b22f-47e8-9535-fac381f088ce · outbound

This paper cites AI-based anomaly detection for clinical-grade histopathological diagnostics.

Towards Robust Foundation Models for Digital Pathology AI-based anomaly detection for clinical-grade histopathological diagnostics

Reference 17

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Observation eceabca4-9410-4505-aa19-4ab7268892b2 · outbound

This paper cites DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole- slide images in colorectal and breast cancer.

Towards Robust Foundation Models for Digital Pathology DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole- slide images in colorectal and breast cancer

Reference 18

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Observation 81034673-5327-4a97-ba50-429fea639c2e · outbound

This paper cites RudolfV: A Foundation Model by Pathologists for Pathologists.

Towards Robust Foundation Models for Digital Pathology RudolfV: A Foundation Model by Pathologists for Pathologists

Reference 19

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Observation 77ae8eec-9c60-4169-80fb-a510e535642c · outbound

This paper cites HEST-1k: A dataset for spatial transcriptomics and histology image analysis.

Towards Robust Foundation Models for Digital Pathology HEST-1k: A dataset for spatial transcriptomics and histology image analysis

Reference 20

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Observation 965397e5-0e09-4703-8bcb-aad4332c9c72 · outbound

This paper cites Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.

Towards Robust Foundation Models for Digital Pathology Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection

Reference 21

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Observation d2046e25-c831-4fda-bd36-7d11cf919ee2 · outbound

This paper cites A benchmarking crisis in biomedical machine learning.

Towards Robust Foundation Models for Digital Pathology A benchmarking crisis in biomedical machine learning

Reference 22

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Observation c88452cd-0ed8-475f-af9a-cd7fe2c4f6b4 · outbound

This paper cites eva: Evaluation framework for pathology foundation models.

Towards Robust Foundation Models for Digital Pathology eva: Evaluation framework for pathology foundation models

Reference 23

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Observation 0e49b7f7-2683-4c79-bb50-be67610e1e40 · outbound

This paper cites Accelerating Data Processing and Benchmarking of AI Models for Pathology.

Towards Robust Foundation Models for Digital Pathology Accelerating Data Processing and Benchmarking of AI Models for Pathology

Reference 24

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Observation a1ccd220-ed76-4d38-8915-88c1d2b4c256 · outbound

This paper cites Molecular-driven Foundation Model for Oncologic Pathology.

Towards Robust Foundation Models for Digital Pathology Molecular-driven Foundation Model for Oncologic Pathology

Reference 25

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Observation 2ec8ae78-0c15-491b-9ad4-1f11b2ee84dc · outbound

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

Towards Robust Foundation Models for Digital Pathology PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology

Reference 26

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Observation a6093123-54b0-4201-96ce-62f5ae2700ea · outbound

This paper cites Benchmarking pathology foundation models: Adaptation strategies and scenarios.

Towards Robust Foundation Models for Digital Pathology Benchmarking pathology foundation models: Adaptation strategies and scenarios

Reference 27

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Observation 4fd6a44c-fff8-498f-8c0c-8ed6016e4372 · outbound

This paper cites Foundation models – a panacea for artificial intelligence in pathology? arXiv preprint arXiv:2502.21264, 2025.

Towards Robust Foundation Models for Digital Pathology Foundation models – a panacea for artificial intelligence in pathology? arXiv preprint arXiv:2502.21264, 2025

Reference 28

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Observation 499a5265-457b-4378-8113-9c82eb53116f · outbound

This paper cites Evaluating vision and pathology foundation models for computational pathology: A comprehensive benchmark study.

Towards Robust Foundation Models for Digital Pathology Evaluating vision and pathology foundation models for computational pathology: A comprehensive benchmark study

Reference 29

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Observation d9380591-83c3-4153-bd45-700d8536723c · outbound

This paper cites The impact of site-specific digital histology signatures on deep learning model accuracy and bias.

Towards Robust Foundation Models for Digital Pathology The impact of site-specific digital histology signatures on deep learning model accuracy and bias

Reference 30

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Observation 88e127d0-761d-40c4-afc7-42dd4f37c633 · outbound

This paper cites Toward explainable artificial intelligence for precision pathology.

Towards Robust Foundation Models for Digital Pathology Toward explainable artificial intelligence for precision pathology

Reference 31

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Observation 4bf9c2b5-8c4c-4ec4-bcb6-dcd8018fa2ca · outbound

This paper cites Tackling the widespread and critical impact of batch effects in high-throughput data.

Towards Robust Foundation Models for Digital Pathology Tackling the widespread and critical impact of batch effects in high-throughput data

Reference 32

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Observation e34ad330-f0be-4992-a536-b694ce5629e8 · outbound

This paper cites Why batch effects matter in omics data, and how to avoid them.

Towards Robust Foundation Models for Digital Pathology Why batch effects matter in omics data, and how to avoid them

Reference 33

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This paper cites Are batch effects still relevant in the age of big data? Trends in Biotechnology, 40(9):1029–1040, 2022.

Towards Robust Foundation Models for Digital Pathology Are batch effects still relevant in the age of big data? Trends in Biotechnology, 40(9):1029–1040, 2022

Reference 34

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Observation 5ac7dabf-ad13-49be-8508-3a69cb9609ff · outbound

This paper cites AI for radiographic COVID-19 detection selects shortcuts over signal.

Towards Robust Foundation Models for Digital Pathology AI for radiographic COVID-19 detection selects shortcuts over signal

Reference 35

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This paper cites Explainable AI reveals Clever Hans effects in unsupervised learning models.

Towards Robust Foundation Models for Digital Pathology Explainable AI reveals Clever Hans effects in unsupervised learning models

Reference 36

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This paper cites Standardizing flow cytometry immunophenotyping analysis from the human immunophenotyping consortium.

Towards Robust Foundation Models for Digital Pathology Standardizing flow cytometry immunophenotyping analysis from the human immunophenotyping consortium

Reference 37

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Observation c77244bc-eeb8-4704-854d-9be5aa2b722d · outbound

This paper cites Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.

Towards Robust Foundation Models for Digital Pathology Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial

Reference 38

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Observation bb47bcec-3935-40e9-812c-f7a8698ce082 · outbound

This paper cites Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies.

Towards Robust Foundation Models for Digital Pathology Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies

Reference 39

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This paper cites Do histopathological foundation models eliminate batch effects? A comparative study.

Towards Robust Foundation Models for Digital Pathology Do histopathological foundation models eliminate batch effects? A comparative study

Reference 40

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Observation 2702457f-753d-404b-a59a-0d10c02b2b17 · outbound

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

Towards Robust Foundation Models for Digital Pathology Current Pathology Foundation Models are unrobust to Medical Center Differences

Reference 41

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Observation c0dc232e-9d3d-49b9-9e01-6ffbf12aff92 · outbound

This paper cites Distilling foundation models for robust and efficient models in digital pathology.

Towards Robust Foundation Models for Digital Pathology Distilling foundation models for robust and efficient models in digital pathology

Reference 42

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Observation 5419f89f-ada5-41a3-adcb-fd9d73d01cb9 · outbound

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

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

Reference 43

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Observation fe68b9f5-7932-451d-8f7c-24f5485571a8 · outbound

This paper cites Physical color calibration of digital pathology scanners for robust artificial intelligence–assisted cancer diagnosis.Modern Pathology, 38(5):100715, 2025.

Towards Robust Foundation Models for Digital Pathology Physical color calibration of digital pathology scanners for robust artificial intelligence–assisted cancer diagnosis.Modern Pathology, 38(5):100715, 2025

Reference 44

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This paper cites MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification.

Towards Robust Foundation Models for Digital Pathology MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification

Reference 45

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Observation fcf1f849-0a26-4605-a87f-072412887d5b · outbound

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Towards Robust Foundation Models for Digital Pathology Color transfer between images

Reference 46

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This paper cites Adjusting batch effects in microarray expression data using empirical bayes methods.

Towards Robust Foundation Models for Digital Pathology Adjusting batch effects in microarray expression data using empirical bayes methods

Reference 47

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Observation 1f120072-d05d-454e-b491-87eafc402a59 · outbound

This paper cites pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods.

Towards Robust Foundation Models for Digital Pathology pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.244286Z

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Observation 21061eb1-1ec0-47aa-90d5-b5581e7e095f · outbound

This paper cites Deep feature batch correction using ComBat for machine learning applications in computational pathology.

Towards Robust Foundation Models for Digital Pathology Deep feature batch correction using ComBat for machine learning applications in computational pathology

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.236422Z

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Observation 8bf3da4b-cf40-4f6b-b8c8-758aa0afbb49 · outbound

This paper cites Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.

Towards Robust Foundation Models for Digital Pathology Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Reference 50

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Observation 34e3a783-0dbc-4f27-a088-62cac3797ab9 · outbound

This paper cites From detection of individual metastases to classification of lymph node status at the patient level: The CAMELYON17 challenge.

Towards Robust Foundation Models for Digital Pathology From detection of individual metastases to classification of lymph node status at the patient level: The CAMELYON17 challenge

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.219384Z

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Observation 1c577f4e-fb10-486d-a1d4-d9259ba191ce · outbound

This paper cites Universal encoding of pan-cancer histology by deep texture representations.

Towards Robust Foundation Models for Digital Pathology Universal encoding of pan-cancer histology by deep texture representations

Reference 52

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Observation 083ff3e3-c85e-4918-a733-506a186cde68 · outbound

This paper cites Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study.

Towards Robust Foundation Models for Digital Pathology Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study

Reference 53

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This paper cites Unmasking Clever Hans predictors and assessing what machines really learn.Nature Communications, 10(1):1096, 2019.

Towards Robust Foundation Models for Digital Pathology Unmasking Clever Hans predictors and assessing what machines really learn.Nature Communications, 10(1):1096, 2019

Reference 54

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Observation c79c2373-4a9e-42df-ab81-3d885eb96c3a · outbound

This paper cites Shortcut learning in deep neural networks.

Towards Robust Foundation Models for Digital Pathology Shortcut learning in deep neural networks

Reference 55

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Towards Robust Foundation Models for Digital Pathology Unresolved cited work

Reference 56

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Observation 5e8775b9-dfa9-4f84-b647-83139a43cd02 · outbound

This paper cites Demographic bias in misdiagnosis by computational pathology models.

Towards Robust Foundation Models for Digital Pathology Demographic bias in misdiagnosis by computational pathology models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.738183Z

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This paper cites Detecting shortcut learning for fair medical AI using shortcut testing.

Towards Robust Foundation Models for Digital Pathology Detecting shortcut learning for fair medical AI using shortcut testing

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.168024Z

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

Observation ccffece4-90a4-416a-bd4b-f03aeee2c8bc · outbound

This paper cites Durbin, Xiao-Cheng Wu, Antoinette Stroup, Jennifer Doherty, Stephen Schwartz, Charles Wiggins, Mark Damesyn, Linda Coyle, Lynne Penberthy, Georgia D.

Towards Robust Foundation Models for Digital Pathology Durbin, Xiao-Cheng Wu, Antoinette Stroup, Jennifer Doherty, Stephen Schwartz, Charles Wiggins, Mark Damesyn, Linda Coyle, Lynne Penberthy, Georgia D

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.161095Z

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Observation 3d5276a3-ee41-4996-a280-1a89a3465de3 · outbound

This paper cites A data augmentation methodology to reduce the class imbalance in histopathology images.

Towards Robust Foundation Models for Digital Pathology A data augmentation methodology to reduce the class imbalance in histopathology images

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.154528Z

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Observation 5b85d961-5156-45c1-b8bc-1af40b87790b · outbound

This paper cites Tizhoosh and Liron Pantanowitz.

Towards Robust Foundation Models for Digital Pathology Tizhoosh and Liron Pantanowitz

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.147910Z

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Observation f902f785-3195-44c0-aa18-a0cc46ac03a7 · outbound

This paper cites Validation of histopathology foundation models through whole slide image retrieval.

Towards Robust Foundation Models for Digital Pathology Validation of histopathology foundation models through whole slide image retrieval

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.140376Z

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Observation 89b96e4b-53f9-4b20-ad08-0d042aa8f4a8 · outbound

This paper cites Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer.

Towards Robust Foundation Models for Digital Pathology Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.132875Z

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Observation f4907404-eff8-4cce-b99d-4c18efd62f3f · outbound

This paper cites Depicter: Deep represen- tation clustering for histology annotation.

Towards Robust Foundation Models for Digital Pathology Depicter: Deep represen- tation clustering for histology annotation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.124688Z

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Observation e86923c7-da87-4587-919f-1ef398f9714a · outbound

This paper cites Lempitsky.

Towards Robust Foundation Models for Digital Pathology Lempitsky

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.117832Z

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

Observation f40ae249-97a3-4930-9fcd-77bfa9c3d484 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Towards Robust Foundation Models for Digital Pathology Learning transferable visual models from natural language supervision

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.110390Z

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Observation 133a2051-f17b-4239-a184-8b328c8a7414 · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

Towards Robust Foundation Models for Digital Pathology Scaling up visual and vision-language representation learning with noisy text supervision

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.103427Z

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Observation 9abefb9e-050e-4c5d-9dda-7dab7022ab46 · outbound

This paper cites GPT-4 Technical Report.

Towards Robust Foundation Models for Digital Pathology GPT-4 Technical Report

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.762332Z

Source-reported events for the cited work

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Observation b1383610-2aeb-4b55-a04b-e38ca858d025 · outbound

This paper cites A comprehensive survey on pretrained foundation models: A history from bert to chatgpt.

Towards Robust Foundation Models for Digital Pathology A comprehensive survey on pretrained foundation models: A history from bert to chatgpt

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.764460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc85b691-d734-4151-88c7-d07b2f9436e1 · outbound

This paper cites Large language models encode clinical knowledge.

Towards Robust Foundation Models for Digital Pathology Large language models encode clinical knowledge

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.766702Z

Source-reported events for the cited work

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Observation 840fe874-7152-4cbf-aeb9-9859ec37c763 · outbound

This paper cites Molecular simulations with a pretrained neural network and universal pairwise force fields.

Towards Robust Foundation Models for Digital Pathology Molecular simulations with a pretrained neural network and universal pairwise force fields

Reference 71

Resolution: verified exact; doi, observed 2026-08-06T15:11:14.880059Z

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Observation b958b2e9-c707-49a3-ab54-0768950f6557 · outbound

This paper cites Evaluation of the mace force field architecture: From medicinal chemistry to materials science.

Towards Robust Foundation Models for Digital Pathology Evaluation of the mace force field architecture: From medicinal chemistry to materials science

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.087081Z

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Observation de46e2ac-37cb-41f4-94d0-1b14e5013510 · outbound

This paper cites Foundation models defining a new era in vision: A survey and outlook.

Towards Robust Foundation Models for Digital Pathology Foundation models defining a new era in vision: A survey and outlook

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.080987Z

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

Observation 12075f69-26db-411e-8f6e-6f51a6607242 · outbound

This paper cites A Foundation Model for Spatial Proteomics.

Towards Robust Foundation Models for Digital Pathology A Foundation Model for Spatial Proteomics

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.775019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cffbc137-6b1c-4820-9cde-11d091f8c2b6 · outbound

This paper cites Aligning Machine and Human Visual Representations across Abstraction Levels.

Towards Robust Foundation Models for Digital Pathology Aligning Machine and Human Visual Representations across Abstraction Levels

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:11:15.062302Z

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Observation 659771c7-491f-48e3-8724-530bf5db2b29 · outbound

This paper cites An intentional approach to managing bias in general purpose embedding models.

Towards Robust Foundation Models for Digital Pathology An intentional approach to managing bias in general purpose embedding models

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.074399Z

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

Observation b763a021-f2b4-4a24-bf12-912337810ac6 · outbound

This paper cites Attention-based deep multiple instance learning.

Towards Robust Foundation Models for Digital Pathology Attention-based deep multiple instance learning

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.067470Z

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

Observation 0d40a817-5daa-4e51-9ba7-82dece06f212 · outbound

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

Towards Robust Foundation Models for Digital Pathology Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.061112Z

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Observation 6cc5de87-bd87-484c-a57d-080d205294fa · outbound

This paper cites Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Towards Robust Foundation Models for Digital Pathology Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.054314Z

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

Observation 6c82363e-20a1-4d40-a378-bd3281e80cf7 · outbound

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

Towards Robust Foundation Models for Digital Pathology A whole-slide foundation model for digital pathology from real-world data

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.047430Z

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Observation 512d695b-6f9a-4a37-b8f1-a8863e4f6bfc · outbound

This paper cites A pathology foundation model for cancer diagnosis and prognosis prediction.

Towards Robust Foundation Models for Digital Pathology A pathology foundation model for cancer diagnosis and prognosis prediction

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.041251Z

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

Observation fa3cda3e-d8c7-4bba-ac49-d05f7742b107 · outbound

This paper cites Multimodal Whole Slide Foundation Model for Pathology.

Towards Robust Foundation Models for Digital Pathology Multimodal Whole Slide Foundation Model for Pathology

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.793037Z

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Unavailable: canonical work link unavailable.

Observation 79eb19e4-ac59-468e-8382-7c05eba74a69 · outbound

This paper cites Training language models to follow instructions with human feedback.

Towards Robust Foundation Models for Digital Pathology Training language models to follow instructions with human feedback

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.795566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83d793dc-dcf1-49e0-aba9-5f01822e198c · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Towards Robust Foundation Models for Digital Pathology Constitutional AI: Harmlessness from AI Feedback

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.797742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc6d0bad-044b-44ea-91f3-6490ee710e8b · outbound

This paper cites Chen, Chengkuan Chen, Yicong Li, Tiffany Y.

Towards Robust Foundation Models for Digital Pathology Chen, Chengkuan Chen, Yicong Li, Tiffany Y

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.030884Z

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

Observation ef7446f9-e59b-4d07-9934-49f4376730f1 · outbound

This paper cites Retccl: Clustering-guided contrastive learning for whole-slide image retrieval.

Towards Robust Foundation Models for Digital Pathology Retccl: Clustering-guided contrastive learning for whole-slide image retrieval

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.017146Z

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

Observation 33974121-19d0-4ea9-908f-d446e51234ea · outbound

This paper cites Transformer-based unsupervised contrastive learning for histopathological image classification.

Towards Robust Foundation Models for Digital Pathology Transformer-based unsupervised contrastive learning for histopathological image classification

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.806920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8660ed46-75c9-493d-a7cb-416a6e2a8b03 · outbound

This paper cites Benchmarking self- supervised learning on diverse pathology datasets.

Towards Robust Foundation Models for Digital Pathology Benchmarking self- supervised learning on diverse pathology datasets

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.006802Z

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

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

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

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 6c3936a1-aa59-46e6-a92a-f93990c08306 · outbound

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

Towards Robust Foundation Models for Digital Pathology Scaling self-supervised learning for histopathology with masked image modeling

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:16.000213Z

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

Observation 3873726f-9931-40e6-b33c-8a0b6e80b401 · outbound

This paper cites Phikon-v2, A large and public feature extractor for biomarker prediction.

Towards Robust Foundation Models for Digital Pathology Phikon-v2, A large and public feature extractor for biomarker prediction

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.815838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 451b9829-187c-4ba1-8c1d-dd79ee6df8b0 · outbound

This paper cites A visual-language foundation model for computational pathology.

Towards Robust Foundation Models for Digital Pathology A visual-language foundation model for computational pathology

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:15.993278Z

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

Observation effb3c6f-d695-4e92-b7c6-b4da78206291 · outbound

This paper cites A vision–language foundation model for precision oncology.

Towards Robust Foundation Models for Digital Pathology A vision–language foundation model for precision oncology

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:15.986195Z

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 e4aeee14-ec58-4180-82de-c82bf854833f · outbound

This paper cites an unresolved cited work.

Towards Robust Foundation Models for Digital Pathology Unresolved cited work

Reference 94

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:11:15.979902Z

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Observation f17ea63b-c8d8-4f0b-8ea0-49fbdeb18a7e · outbound

This paper cites Fix and J.L.

Towards Robust Foundation Models for Digital Pathology Fix and J.L

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:15.972916Z

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

Observation e83233c2-a233-4f81-b596-fa6a6a84d2dd · outbound

This paper cites Cover and P.

Towards Robust Foundation Models for Digital Pathology Cover and P

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:15.966083Z

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 a84c45ad-7a71-4419-b078-fa3758d6454f · outbound

This paper cites Silhouettes: a graphical aid to the interpretation and validation of cluster analysis.

Towards Robust Foundation Models for Digital Pathology Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.828832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5cab708-c28c-47a1-beb9-9b9bfb8e94e8 · outbound

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Towards Robust Foundation Models for Digital Pathology Comparing partitions

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:15.955525Z

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 6a75cae4-e278-4e5e-9a63-e19b74156eb0 · outbound

This paper cites Domain generalization in computational pathology: survey and guidelines.

Towards Robust Foundation Models for Digital Pathology Domain generalization in computational pathology: survey and guidelines

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:15.949046Z

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

Observation 6b8fc984-b201-462d-bfe7-b6dc696af26b · outbound

This paper cites Marron, David Borland, John Woosley, Xiaojun Guan, Charles Schmitt, and Nancy Thomas.

Towards Robust Foundation Models for Digital Pathology Marron, David Borland, John Woosley, Xiaojun Guan, Charles Schmitt, and Nancy Thomas

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:11:15.866958Z

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Pith citing papers

Observation c6b42115-f74d-46e6-9651-be03aee2950a · inbound

Mitigating Clever Hans Strategies in Image Classifiers through Generating Counterexamples cites this paper.

Mitigating Clever Hans Strategies in Image Classifiers through Generating Counterexamples Towards Robust Foundation Models for Digital Pathology

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:50:56.480041Z

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Observation db476ff9-2ce4-4b9c-b64b-b0cc23028f11 · inbound

Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport cites this paper.

Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport Towards Robust Foundation Models for Digital Pathology

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T17:31:08.718483Z

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 9c4f7529-2ff0-449b-85e9-ca33ea9213d4 · inbound

When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework cites this paper.

When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework Towards Robust Foundation Models for Digital Pathology

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T22:52:45.190399Z

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

Observation 505c76c8-8e34-40c1-a662-cf110aabba14 · inbound

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology cites this paper.

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology Towards Robust Foundation Models for Digital Pathology

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:06:55.835163Z

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 13a6bf1b-175a-4867-9a93-4f3545f46834 · inbound

In-Context Multiple Instance Learning cites this paper.

In-Context Multiple Instance Learning Towards Robust Foundation Models for Digital Pathology

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:06:56.371270Z

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 4c6b44ec-c244-42a5-a33b-6cabbbba4255 · inbound

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

DaX: Learning General Pathology Representations Across Scales Towards Robust Foundation Models for Digital Pathology

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T20:17:22.237322Z

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 5f78a882-7163-45d4-83a3-095ed2522f07 · inbound

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models cites this paper.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Towards Robust Foundation Models for Digital Pathology

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.290831Z

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Unavailable: canonical work link unavailable.