Robustifying pathology foundation models via fine-tuning | Paper Citation Record

Observation 8aceb108-8ce0-4c12-9ff7-7f5bbee64e8b · outbound

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

Robustifying pathology foundation models via fine-tuning Atlas 2 -- Foundation models for clinical deployment

Reference 1

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This paper cites an unresolved cited work.

Robustifying pathology foundation models via fine-tuning Unresolved cited work

Reference 2

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Robustifying pathology foundation models via fine-tuning Unresolved cited work

Reference 3

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Observation f8f16534-b4fd-49ab-b540-186da620e572 · outbound

This paper cites H-optimus-1.https://huggingface.co/bioptimus/H-optimus-1, 2025.

Robustifying pathology foundation models via fine-tuning H-optimus-1.https://huggingface.co/bioptimus/H-optimus-1, 2025

Reference 4

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Observation c9a5602e-58d0-4b5c-b4fb-b906e5f978c3 · outbound

This paper cites Pathology Foundation Models are Scanner Sensitive: Benchmark and Mitigation with Contrastive ScanGen Loss.

Robustifying pathology foundation models via fine-tuning Pathology Foundation Models are Scanner Sensitive: Benchmark and Mitigation with Contrastive ScanGen Loss

Reference 5

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Observation 506bac05-fa9e-4104-881f-fd70a36537a6 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Robustifying pathology foundation models via fine-tuning Emerging properties in self-supervised vision transformers

Reference 6

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Observation fd55dda7-6c42-4407-9655-98c2ec308f3a · outbound

This paper cites Steiner, Tapabrata Chakraborti, and Adrienne M.

Robustifying pathology foundation models via fine-tuning Steiner, Tapabrata Chakraborti, and Adrienne M

Reference 7

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Observation ed936448-7f55-4a7d-9bfd-7512bfb511b1 · outbound

This paper cites Chen, Tong Ding, Ming Y.

Robustifying pathology foundation models via fine-tuning Chen, Tong Ding, Ming Y

Reference 8

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Observation e966bc97-c6ad-495b-b966-6e51da3d1eb3 · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Robustifying pathology foundation models via fine-tuning Improved Baselines with Momentum Contrastive Learning

Reference 9

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Observation f0b74b54-b86a-4e5f-ab56-33222dcb371a · outbound

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Robustifying pathology foundation models via fine-tuning Unresolved cited work

Reference 10

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Observation f110953b-a401-482c-8469-66b73dbe9b03 · outbound

This paper cites Nicholson, Jean-Yves Blay, Françoise Galateau-Sallé, Gilles Wainrib, and Thomas Clozel.

Robustifying pathology foundation models via fine-tuning Nicholson, Jean-Yves Blay, Françoise Galateau-Sallé, Gilles Wainrib, and Thomas Clozel

Reference 11

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Observation 7af217e0-b36d-45ec-a16a-c85b3b835c8d · outbound

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

Robustifying pathology foundation models via fine-tuning Current Pathology Foundation Models are unrobust to Medical Center Differences

Reference 12

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Observation 46b02d94-6720-4cd6-95ef-07007535a440 · outbound

This paper cites Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histology.

Robustifying pathology foundation models via fine-tuning Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histology

Reference 13

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Observation d883c8ed-5949-4055-8d15-af196ea97908 · outbound

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Robustifying pathology foundation models via fine-tuning Unresolved cited work

Reference 14

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Observation 8cdca6ea-044b-4a6d-af64-2b6ea97db438 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Robustifying pathology foundation models via fine-tuning Imagenet: A large-scale hierarchical image database

Reference 15

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Observation 95aee7cd-fb0d-4a16-a5bb-dac7b285cb56 · outbound

This paper cites Multimodal Whole Slide Foundation Model for Pathology.

Robustifying pathology foundation models via fine-tuning Multimodal Whole Slide Foundation Model for Pathology

Reference 16

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Observation ddac4236-11af-4e4e-b66e-f00f1f413090 · outbound

This paper cites Medi: Metadata-guided diffusion models for mitigating biases in tumor classification.

Robustifying pathology foundation models via fine-tuning Medi: Metadata-guided diffusion models for mitigating biases in tumor classification

Reference 17

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Observation fac29179-0745-47ae-a2fb-aec689d45cf8 · outbound

This paper cites Scaling self-supervised learning for histopathology with masked image modeling.medRxiv, 2023.

Robustifying pathology foundation models via fine-tuning Scaling self-supervised learning for histopathology with masked image modeling.medRxiv, 2023

Reference 18

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Observation d244f055-cc21-4a80-97fa-c5cac302d08e · outbound

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

Robustifying pathology foundation models via fine-tuning Phikon-v2, A large and public feature extractor for biomarker prediction

Reference 19

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Observation 02ed4d44-3261-47bf-a23a-fcb77fc21c80 · outbound

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

Robustifying pathology foundation models via fine-tuning Distilling foundation models for robust and efficient models in digital pathology

Reference 20

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Observation 27c4d916-2674-4320-9b8c-1315b8061cc3 · outbound

This paper cites Domain-adversarial training of neural networks.Journal of Machine Learning Research, 17(59):1–35, 2016.

Robustifying pathology foundation models via fine-tuning Domain-adversarial training of neural networks.Journal of Machine Learning Research, 17(59):1–35, 2016

Reference 21

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Observation 5e6cbd0c-1c71-49a6-943b-639c77ad2c5b · outbound

This paper cites Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings.

Robustifying pathology foundation models via fine-tuning Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings

Reference 22

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Observation 896ceb93-b3a1-4a30-ac48-266d7f9609c6 · outbound

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

Robustifying pathology foundation models via fine-tuning Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts

Reference 23

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Observation f4d30dcb-5385-4db6-a90b-1fa80da60428 · outbound

This paper cites Enabling clinical use of foundation models for computational pathology.

Robustifying pathology foundation models via fine-tuning Enabling clinical use of foundation models for computational pathology

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:31:21.703973Z

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Observation b658b622-30cb-45e9-ac03-20a29f9eeadf · outbound

This paper cites Howard, James Dolezal, Sara Kochanny, Jefree Schulte, Heather Chen, Lara Heij, Dezheng Huo, Rita Nanda, Olufunmilayo I.

Robustifying pathology foundation models via fine-tuning Howard, James Dolezal, Sara Kochanny, Jefree Schulte, Heather Chen, Lara Heij, Dezheng Huo, Rita Nanda, Olufunmilayo I

Reference 25

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Observation 87e181d0-81f6-4587-8267-02108b91277b · outbound

This paper cites Knowledge-guided adaptation of pathology foundation models effectively improves cross-domain generalization and demographic fairness.Nature Communications, 16:11485, 2025.

Robustifying pathology foundation models via fine-tuning Knowledge-guided adaptation of pathology foundation models effectively improves cross-domain generalization and demographic fairness.Nature Communications, 16:11485, 2025

Reference 26

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Observation 48e724e9-03b2-49fd-a034-566cdb5c54c3 · outbound

This paper cites Tomczak, and Max Welling.

Robustifying pathology foundation models via fine-tuning Tomczak, and Max Welling

Reference 27

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Observation 3879043a-17a5-487b-a8e3-ddd5f6f2844d · outbound

This paper cites Domain Generalization in Computational Pathology: Survey and Guidelines.

Robustifying pathology foundation models via fine-tuning Domain Generalization in Computational Pathology: Survey and Guidelines

Reference 28

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Observation 497eca0e-ff09-4f73-a1d4-c23c648cd72d · outbound

This paper cites HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis.

Robustifying pathology foundation models via fine-tuning HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis

Reference 29

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Observation afe27ace-84d7-49a6-a2e4-85fd2a12893c · outbound

This paper cites an unresolved cited work.

Robustifying pathology foundation models via fine-tuning Unresolved cited work

Reference 30

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Observation c28583b6-c084-4d50-b9c1-1fee2e9414ba · outbound

This paper cites Training state-of-the-art pathology foundation models with orders of magnitude less data.

Robustifying pathology foundation models via fine-tuning Training state-of-the-art pathology foundation models with orders of magnitude less data

Reference 31

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Observation 84fecc33-bbf9-46b5-9d19-2cbf20925c7b · outbound

This paper cites Pearson, Niels Halama, Dirk Jäger, Jeremias Krause, Sven H.

Robustifying pathology foundation models via fine-tuning Pearson, Niels Halama, Dirk Jäger, Jeremias Krause, Sven H

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Observation e43052c4-5f2f-4994-9f6a-7a760082fe5c · outbound

This paper cites Viergever, and Josien P.

Robustifying pathology foundation models via fine-tuning Viergever, and Josien P

Reference 33

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Observation 4234f504-e297-46cb-b9f8-7e2dca856239 · outbound

This paper cites Towards Robust Foundation Models for Digital Pathology.

Robustifying pathology foundation models via fine-tuning Towards Robust Foundation Models for Digital Pathology

Reference 34

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Observation 31420f95-7c11-4461-881f-ed480911111d · outbound

This paper cites Universal encoding of pan-cancer histology by deep texture representations.Cell Reports, 38(9):110424, 2022.

Robustifying pathology foundation models via fine-tuning Universal encoding of pan-cancer histology by deep texture representations.Cell Reports, 38(9):110424, 2022

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Resolution: verified exact; doi, observed 2026-08-15T15:31:20.747328Z

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Observation e047caaf-1491-4170-8711-ac00d46e2f0b · outbound

This paper cites Beyond Diagnostic Performance: Revealing and Quantifying Ethical Risks in Pathology Foundation Models.

Robustifying pathology foundation models via fine-tuning Beyond Diagnostic Performance: Revealing and Quantifying Ethical Risks in Pathology Foundation Models

Reference 36

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Observation 0bc94b37-5ab7-4859-b071-ad25fddee4f4 · outbound

This paper cites Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation.

Robustifying pathology foundation models via fine-tuning Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 37

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Observation dced19ec-fec3-4731-9d9e-155d90c12bd1 · outbound

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Robustifying pathology foundation models via fine-tuning Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:31:22.256489Z

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Observation d0dfdf2b-74b3-40d5-af9f-20a254a0bdfb · outbound

This paper cites Thunder: Tile-level histopathology image understanding benchmark.Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2025.

Robustifying pathology foundation models via fine-tuning Thunder: Tile-level histopathology image understanding benchmark.Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2025

Reference 39

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Observation fd90b84f-d5d8-4cfa-9b40-1edf50a8d265 · outbound

This paper cites fmmap: A framework reducing site-bias batch effect from foundation models in pathology.

Robustifying pathology foundation models via fine-tuning fmmap: A framework reducing site-bias batch effect from foundation models in pathology

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:31:22.243227Z

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Observation 0e49b96f-4d63-4100-872b-846f1233b252 · outbound

This paper cites an unresolved cited work.

Robustifying pathology foundation models via fine-tuning Unresolved cited work

Reference 41

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Observation 649c790e-efb1-4d8c-ac15-94aaf7994f70 · outbound

This paper cites Registered multi-device/staining histology image dataset for domain-agnostic machine learning models.Scientific Data, 11(1):330, 2024.

Robustifying pathology foundation models via fine-tuning Registered multi-device/staining histology image dataset for domain-agnostic machine learning models.Scientific Data, 11(1):330, 2024

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Observation e23c3604-4aae-4140-8f5b-9c0c95b31e4c · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Robustifying pathology foundation models via fine-tuning DINOv2: Learning Robust Visual Features without Supervision

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Observation 1a96ab59-ac38-4a03-a8b0-2b5deca53022 · outbound

This paper cites ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology.

Robustifying pathology foundation models via fine-tuning ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology

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Resolution: verified exact; local_arxiv, observed 2026-08-15T15:31:21.393991Z

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Observation c1d1223a-4174-4f3a-a713-1ea3f85c5b41 · outbound

This paper cites Scorpion: Addressing scanner-induced variability in histopathology.

Robustifying pathology foundation models via fine-tuning Scorpion: Addressing scanner-induced variability in histopathology

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Resolution: verified exact; raw_fallback, observed 2026-08-15T15:31:21.364484Z

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Observation bd42d71a-a3ab-484a-8d5e-f36042350928 · outbound

This paper cites Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers.

Robustifying pathology foundation models via fine-tuning Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:31:21.294508Z

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Observation c03dba9c-5249-4804-b36f-fb48c57e08fc · outbound

This paper cites Color transfer between images.IEEE Computer Graphics and Applications, 21(5):34–41, 2001.

Robustifying pathology foundation models via fine-tuning Color transfer between images.IEEE Computer Graphics and Applications, 21(5):34–41, 2001

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Observation 856cb718-f5fe-4a09-8257-22d6fc848a5b · outbound

This paper cites H-optimus-0.

Robustifying pathology foundation models via fine-tuning H-optimus-0

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:31:22.227893Z

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Observation ba4fbccc-a460-4cd2-a7f9-d7a41ce2dc5d · outbound

This paper cites A deep learning model to predict rna-seq expression of tumours from whole slide images.Nature Communications, 11(1):3877, 2020.

Robustifying pathology foundation models via fine-tuning A deep learning model to predict rna-seq expression of tumours from whole slide images.Nature Communications, 11(1):3877, 2020

Reference 49

Resolution: verified exact; doi, observed 2026-08-15T15:31:20.707004Z

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Observation 84bf1f05-fd31-4c50-a5b2-6049ac04ed7a · outbound

This paper cites Validation of msintuit as an ai-based pre-screening tool for msi detection from colorectal cancer histology slides.Nature Communications, 14(1):6695, 2023.

Robustifying pathology foundation models via fine-tuning Validation of msintuit as an ai-based pre-screening tool for msi detection from colorectal cancer histology slides.Nature Communications, 14(1):6695, 2023

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Resolution: verified exact; doi, observed 2026-08-15T15:31:20.718984Z

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Observation 418c773c-7d40-47fd-a5f2-7b1501a9cee5 · outbound

This paper cites TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification.

Robustifying pathology foundation models via fine-tuning TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

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Observation 7ab9a723-dd45-479d-8042-3b80f20a497a · outbound

This paper cites RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization.

Robustifying pathology foundation models via fine-tuning RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:31:21.192913Z

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Observation 6b618dfb-9d1d-42f6-a4af-ee62e210b2a0 · outbound

This paper cites Schönpflug, Nikki van den Berg, Sonali Andani, Nanda Horeweg, Jurriaan Barkey Wolf, Tjalling Bosse, Viktor H.

Robustifying pathology foundation models via fine-tuning Schönpflug, Nikki van den Berg, Sonali Andani, Nanda Horeweg, Jurriaan Barkey Wolf, Tjalling Bosse, Viktor H

Reference 53

Resolution: verified exact; raw_fallback, observed 2026-08-15T15:31:21.276449Z

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Observation 6f9405f4-8fc7-4a65-bfad-bde97178556d · outbound

This paper cites Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.Medical Image Analysis, 58:101544, 2019.

Robustifying pathology foundation models via fine-tuning Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.Medical Image Analysis, 58:101544, 2019

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Observation f2f8738d-3aaa-4157-a1a8-776f8a55cf8f · outbound

This paper cites Gustafsson, Kajsa Ledesma Eriksson, and Mattias Rantalainen.

Robustifying pathology foundation models via fine-tuning Gustafsson, Kajsa Ledesma Eriksson, and Mattias Rantalainen

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Observation e23835d9-59ae-4b1d-9c1e-b66f809fb9db · outbound

This paper cites DINOv3.

Robustifying pathology foundation models via fine-tuning DINOv3

Reference 56

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Observation aa6dd4d9-1fd2-415d-b8a2-930136010553 · outbound

This paper cites an unresolved cited work.

Robustifying pathology foundation models via fine-tuning Unresolved cited work

Reference 57

Resolution: verified exact; doi, observed 2026-08-15T15:31:20.693517Z

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Observation a310715c-3b3a-414c-a260-e7bbb26f26a8 · outbound

This paper cites Structure-preserving color normalization and sparse stain separation for histological images.IEEE Transactions on Medical Imaging, 35(8):1962–1971, 2016.

Robustifying pathology foundation models via fine-tuning Structure-preserving color normalization and sparse stain separation for histological images.IEEE Transactions on Medical Imaging, 35(8):1962–1971, 2016

Reference 58

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T15:31:21.025082Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fee1d61b-8171-4038-b8a2-8cd0faf4f7b7 · outbound

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

Robustifying pathology foundation models via fine-tuning Beyond the Failures: Rethinking Foundation Models in Pathology

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T15:31:20.598010Z

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Observation b77d72a3-d302-4a4e-a1e3-230525de1a56 · outbound

This paper cites an unresolved cited work.

Robustifying pathology foundation models via fine-tuning Unresolved cited work

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T15:31:20.613329Z

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Observation 76240983-e7b0-4f01-9228-4d75eefcb837 · outbound

This paper cites Wright, Ari Robicsek, Brian Piening, Carlo Bifulco, Sheng Wang, and Hoifung Poon.

Robustifying pathology foundation models via fine-tuning Wright, Ari Robicsek, Brian Piening, Carlo Bifulco, Sheng Wang, and Hoifung Poon

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:31:22.212105Z

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Observation 58133627-f9fe-4bc9-9fd2-0859e3752e46 · outbound

This paper cites A pathology foundation model for cancer diagnosis and prognosis prediction.Nature, 634(8035):970–978, 2024.

Robustifying pathology foundation models via fine-tuning A pathology foundation model for cancer diagnosis and prognosis prediction.Nature, 634(8035):970–978, 2024

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T15:31:20.609479Z

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Observation 050153ac-0175-4e58-89ff-063d1e519e81 · outbound

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

Robustifying pathology foundation models via fine-tuning Accelerating Data Processing and Benchmarking of AI Models for Pathology

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T15:31:20.630425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50af31b1-bdf1-4cbd-a4c0-2638c0e1ca13 · outbound

This paper cites ibot: Image bert pre-training with online tokenizer.

Robustifying pathology foundation models via fine-tuning ibot: Image bert pre-training with online tokenizer

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:31:22.180015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 827a093a-7180-4a56-afa7-bcdf31e61e70 · outbound

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

Robustifying pathology foundation models via fine-tuning Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T15:31:20.638978Z

Source-reported events for the cited work

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Observation b820da02-d311-4dce-803f-e99aba36ba7b · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction.

Robustifying pathology foundation models via fine-tuning Barlow twins: Self-supervised learning via redundancy reduction

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:31:22.196863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fd357c22-73ab-4e29-9689-4b6cd8fd280a · outbound

This paper cites an unresolved cited work.

Robustifying pathology foundation models via fine-tuning Unresolved cited work

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-15T15:31:20.407409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c970e6f-51c1-4009-9465-25f010fe6f63 · outbound

This paper cites an unresolved cited work.

Robustifying pathology foundation models via fine-tuning Unresolved cited work

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-15T15:31:20.489587Z

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Unavailable: canonical work link unavailable.

Observation 801c63be-d720-4b57-82d6-6c783cd0cc9f · outbound

This paper cites an unresolved cited work.

Robustifying pathology foundation models via fine-tuning Unresolved cited work

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T15:31:20.621064Z

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