Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology | Paper Citation Record

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

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

Reference 49

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Multimodal Whole Slide Foundation Model for Pathology cites this paper.

Multimodal Whole Slide Foundation Model for Pathology Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 12

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Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification cites this paper.

Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 5

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

Are the Latent Representations of Foundation Models for Pathology Invariant to Rotation? Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 12

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CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology cites this paper.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 61

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

Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit\'e, and Aignostics Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 42

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

Reusable specimen-level inference in computational pathology Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 14

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

A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 48

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

Distilling foundation models for robust and efficient models in digital pathology Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 27

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Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and Classification cites this paper.

Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and Classification Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 30

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

Accelerating Data Processing and Benchmarking of AI Models for Pathology Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 18

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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 Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 33

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The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation Models cites this paper.

The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation Models Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 41

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A Foundation Model for Spatial Proteomics cites this paper.

A Foundation Model for Spatial Proteomics Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 80

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Single GPU Task Adaptation of Pathology Foundation Models for Whole Slide Image Analysis cites this paper.

Single GPU Task Adaptation of Pathology Foundation Models for Whole Slide Image Analysis Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 7

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Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtyping cites this paper.

Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtyping Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 17

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Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images cites this paper.

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 25

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Towards Robust Foundation Models for Digital Pathology cites this paper.

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

Reference 13

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

Pathology Foundation Models are Scanner Sensitive: Benchmark and Mitigation with Contrastive ScanGen Loss Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 26

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Transformer-Based Hematological Malignancy Prediction from Peripheral Blood Smears in a Real-World Cohort cites this paper.

Transformer-Based Hematological Malignancy Prediction from Peripheral Blood Smears in a Real-World Cohort Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 3

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MMAP: A Multi-Magnification and Prototype-Aware Architecture for Predicting Spatial Gene Expression cites this paper.

MMAP: A Multi-Magnification and Prototype-Aware Architecture for Predicting Spatial Gene Expression Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 32

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Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment cites this paper.

Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 8

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

Atlas 2 -- Foundation models for clinical deployment Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 82

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

Enabling clinical use of foundation models for computational pathology Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 4

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BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology cites this paper.

BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 30

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MOOZY: A Patient-First Foundation Model for Computational Pathology cites this paper.

MOOZY: A Patient-First Foundation Model for Computational Pathology Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 105

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A Generative Foundation Model for Multimodal Histopathology cites this paper.

A Generative Foundation Model for Multimodal Histopathology Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 28

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OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA cites this paper.

OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:11:01.259498Z

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PC-MIL: Decoupling Feature Resolution from Supervision Scale in Whole-Slide Learning cites this paper.

PC-MIL: Decoupling Feature Resolution from Supervision Scale in Whole-Slide Learning Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 26

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SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification cites this paper.

SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 25

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Unified Multi-Foundation-Model Slide Representation for Pan-Cancer Recognition and Text-Guided Tumor Localization cites this paper.

Unified Multi-Foundation-Model Slide Representation for Pan-Cancer Recognition and Text-Guided Tumor Localization Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 7

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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 Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 30

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

Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 49

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Retrieval-Guided Generation for Safer Histopathology Image Captioning cites this paper.

Retrieval-Guided Generation for Safer Histopathology Image Captioning Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 13

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Validation of an AI-based end-to-end model for prostate pathology using long-term archived routine samples cites this paper.

Validation of an AI-based end-to-end model for prostate pathology using long-term archived routine samples Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 21

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Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation cites this paper.

Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 4

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

Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 9

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CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval cites this paper.

CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 16

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A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation cites this paper.

A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 18

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Observation dc38623d-ffdb-4007-8f14-0a22fabf6dfc · inbound

A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation cites this paper.

A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 25

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Spatial Transcriptomics-Guided Alignment Enhances Molecular Profiling in Pathology Foundation Model cites this paper.

Spatial Transcriptomics-Guided Alignment Enhances Molecular Profiling in Pathology Foundation Model Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:42:50.020179Z

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Observation c9d3eede-cfd7-483d-8093-f6e2034d7021 · inbound

Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma cites this paper.

Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 2

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A Pathology Foundation Model for Gastric Cancer with Real-World Validation cites this paper.

A Pathology Foundation Model for Gastric Cancer with Real-World Validation Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:46:45.693167Z

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Observation eb9741db-7afd-4481-9502-418c5aef833d · 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 Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:06:55.829512Z

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

Observation c7e31b8d-f960-4acd-8a41-a24c4f404c7b · inbound

LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification cites this paper.

LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 20

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Observation eefdd521-a0c0-43b8-a5d2-54174b8185d2 · inbound

Multi-FRuGaL: Multimodal Flexible Redundancy-aware Decomposed Gated Learning for Cancer Diagnosis and Prognosis cites this paper.

Multi-FRuGaL: Multimodal Flexible Redundancy-aware Decomposed Gated Learning for Cancer Diagnosis and Prognosis Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:27:09.212020Z

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

Observation a43ac807-bb39-497f-925f-72c4059241bc · inbound

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

DaX: Learning General Pathology Representations Across Scales Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 70

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

Observation 5904c94f-4869-4283-9996-2942c30d7dce · inbound

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

STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:27:09.490119Z

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Observation 57522bc0-abf5-4a98-af6c-6c4578e2ef51 · inbound

Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge cites this paper.

Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 135

Resolution: verified exact; arxiv_id, observed 2026-07-02T17:07:12.691114Z

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

Observation c15a3b9f-6b00-410f-a4f2-891e7980a5ca · inbound

SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions cites this paper.

SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:53:16.204277Z

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

Observation 3d32b15a-bf44-4074-8647-396fc1abc681 · inbound

A Multi-modal Agentic Co-pilot for Evidence Grounded Computational Pathology cites this paper.

A Multi-modal Agentic Co-pilot for Evidence Grounded Computational Pathology Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:47:27.830443Z

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Observation 0534e7e8-d7ab-4a38-8d64-fee276dd160e · inbound

JASPR: Joint Spatial Representation learning of histology and spatial genomics for improved virtual genomic screening and clinical prognostication cites this paper.

JASPR: Joint Spatial Representation learning of histology and spatial genomics for improved virtual genomic screening and clinical prognostication Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-06-30T03:04:15.277608Z

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Observation 2486d390-1428-4932-8aab-6ef39813f7d4 · inbound

Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation cites this paper.

Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-06-30T10:04:35.977993Z

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 5f38114e-0711-4401-8405-3308b33014d8 · inbound

Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning cites this paper.

Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 43

Resolution: malformed identifier; arxiv_id, observed 2026-06-30T06:34:18.723943Z

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 b26b8d9e-81cc-49e9-bb63-4e7e98e907cd · inbound

CellPrior-Net: Prior-Guided Nuclei Detection and Classification for H&E Whole-Slide Images cites this paper.

CellPrior-Net: Prior-Guided Nuclei Detection and Classification for H&E Whole-Slide Images Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:56:27.072289Z

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 1048e35a-db99-4050-94ae-0ebdc6140522 · inbound

The Good, the Bad, and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Models cites this paper.

The Good, the Bad, and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Models Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-11T19:27:31.668376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 078a6fb5-11c0-4d2c-b5ef-18887d432f4e · inbound

Multi-Teacher Contrastive Distillation for Edge-Efficient Pathology Foundation Models cites this paper.

Multi-Teacher Contrastive Distillation for Edge-Efficient Pathology Foundation Models Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-11T06:13:30.088916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dfe8458-ba28-44a9-827c-da2d3d266183 · inbound

ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts cites this paper.

ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-13T02:23:52.225787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0d0998a-a058-48e0-aafa-ac7a9913ed04 · inbound

LaGuadia: Language-Guided Adaptive Distillation from Pathology Foundation Models cites this paper.

LaGuadia: Language-Guided Adaptive Distillation from Pathology Foundation Models Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-14T05:58:47.981964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fd893fe-22cf-46b1-9ede-17ae22b4dd06 · inbound

Self-supervision drives representational convergence in medical foundation models more than clinical supervision cites this paper.

Self-supervision drives representational convergence in medical foundation models more than clinical supervision Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-01T10:21:48.635330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 536117bb-de83-43d8-9cdf-d13baf09777a · inbound

HistoFID- Calibrating Frechet-distance evaluation across pathology foundation models cites this paper.

HistoFID- Calibrating Frechet-distance evaluation across pathology foundation models Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T10:56:35.799703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Robustifying pathology foundation models via fine-tuning cites this paper.

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

Unavailable: canonical work link unavailable.

Observation 400efdc6-f583-4d5d-a7f3-5d50c4150ef1 · inbound

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

A Distributional Robustness Margin For Pathology Foundation Models Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 35

Resolution: malformed identifier; no resolver link, observed 2026-08-01T02:19:58.338496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 905860ee-c837-4ea9-92f0-f4ab2ad40c0f · inbound

Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures cites this paper.

Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-31T20:37:50.091403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a8ecefc-9c35-4187-9fea-61864b1497a7 · inbound

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer cites this paper.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.862888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8e70a60-733b-4d82-a6aa-29ca225c79d1 · inbound

Understanding Synergistic Interactions among Pathology Foundation Models via Adaptive Fusion cites this paper.

Understanding Synergistic Interactions among Pathology Foundation Models via Adaptive Fusion Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T00:17:36.222366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf57b5fd-3a7d-410d-abe9-115a57c08f57 · inbound

Assessment of Conditional Diffusion Model for Synthetic Histopathology Image Generation cites this paper.

Assessment of Conditional Diffusion Model for Synthetic Histopathology Image Generation Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T04:25:09.041682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1211aad1-93ba-4fc2-8438-811006b60d52 · inbound

One Model to Magnify Them All: Efficient Scale-Invariant Histopathology via Conditional Normalization and Continuous Magnification Training cites this paper.

One Model to Magnify Them All: Efficient Scale-Invariant Histopathology via Conditional Normalization and Continuous Magnification Training Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T17:57:43.891244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers