Paper Citation Record · LEDGER

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

As of 21 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 4 inbound Pith citation observations for arXiv:2412.12077.

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

pith.paper-citation-record.v1
2412.12077 v1

Coverage vector

measured 68 of 68 reference resolution

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measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: arxiv_reference, observed 2026-05-11T21:36:14.367779Z

Reference resolution

68 of 68 outbound references displayed

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External citation measurements

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Outbound references

Observation 84e11085-e756-4eea-bca0-0a62d1a41668 · outbound

This paper cites Bach: Grand challenge on breast cancer histology im- ages.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Bach: Grand challenge on breast cancer histology im- ages

Reference 1

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Observation 3f672871-288a-4c19-b03b-b5a5d21a911a · outbound

This paper cites Viable and necrotic tumor assessment from whole slide images of os- teosarcoma using machine-learning and deep-learning mod- els.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Viable and necrotic tumor assessment from whole slide images of os- teosarcoma using machine-learning and deep-learning mod- els

Reference 2

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Observation a6d3af83-099c-4325-a0bb-a3b66af49601 · outbound

This paper cites Replication Data for: Automated Gleason grading of prostate cancer tissue microarrays via deep learning., 2018.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Replication Data for: Automated Gleason grading of prostate cancer tissue microarrays via deep learning., 2018

Reference 3

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Observation 92d9d8c4-8bfc-4b9b-b368-cd9d302eff8f · outbound

This paper cites Domain generalization across tumor types, laborato- ries, and species—insights from the 2022 edition of the mi- tosis domain generalization challenge.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Domain generalization across tumor types, laborato- ries, and species—insights from the 2022 edition of the mi- tosis domain generalization challenge

Reference 4

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Observation a3ada1c1-14a8-4374-b359-41a6d09c1360 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 5

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Observation 78092a53-3191-4ab3-9163-65806ca735e8 · outbound

This paper cites Lung and Colon Cancer Histopathological Image Dataset (LC25000).

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Lung and Colon Cancer Histopathological Image Dataset (LC25000)

Reference 6

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Observation 3315ec1d-1183-4167-8ddf-a82204cef343 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Emerg- ing properties in self-supervised vision transformers

Reference 7

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Observation 4913823f-fc5e-4bb7-979d-b56d10364a03 · outbound

This paper cites Wsicaption: Multiple instance generation of pathology reports for gigapixel whole-slide im- ages.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Wsicaption: Multiple instance generation of pathology reports for gigapixel whole-slide im- ages

Reference 8

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Observation 7cd677a4-18ef-4a02-89ea-790a06d32400 · outbound

This paper cites Wsi-vqa: Interpreting whole slide images by gen- erative visual question answering.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Wsi-vqa: Interpreting whole slide images by gen- erative visual question answering

Reference 9

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Observation c89c56be-afb3-4daf-84a7-782dbf5c2aec · outbound

This paper cites Scaling vision transformers to gigapixel images via hierarchical self-supervised learning.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Scaling vision transformers to gigapixel images via hierarchical self-supervised learning

Reference 10

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Observation e0ac6a9d-f05c-4489-b921-8a6b019c6dd5 · outbound

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

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Towards a general-purpose foundation model for computational pathology

Reference 11

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Observation 91403e24-19a9-4b52-8b2d-69b05a95d672 · outbound

This paper cites Instructblip: Towards general- purpose vision-language models with instruction tuning.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Instructblip: Towards general- purpose vision-language models with instruction tuning

Reference 12

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Observation e7875d71-9feb-4155-9991-16ed96275ae8 · outbound

This paper cites Multiple instance cap- tioning: Learning representations from histopathology text- books and articles.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Multiple instance cap- tioning: Learning representations from histopathology text- books and articles

Reference 13

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Observation aca55b67-e7d4-43fb-ad80-5aa1516a0972 · outbound

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

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction

Reference 14

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Observation 70efd34c-a403-4d2d-871f-1df320e7d23c · outbound

This paper cites WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

Reference 15

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Observation 3128e2d6-a9a2-49a9-aec9-85db8d3ca80d · outbound

This paper cites A visual–language foundation model for pathology image analysis using medical twitter.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology A visual–language foundation model for pathology image analysis using medical twitter

Reference 16

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Observation ecca5676-5ace-4ec2-bcd0-e882bbc8099d · outbound

This paper cites Qwen2.5-Coder Technical Report.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Qwen2.5-Coder Technical Report

Reference 17

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Observation b659576d-973e-4452-8118-0e0fdc7e65c7 · outbound

This paper cites A comprehen- sive ai model development framework for consistent gleason grading.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology A comprehen- sive ai model development framework for consistent gleason grading

Reference 18

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Observation 37975be7-7210-4973-9558-51e01cca0fbc · outbound

This paper cites Quilt-1m: One million image-text pairs for histopathology.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Quilt-1m: One million image-text pairs for histopathology

Reference 19

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Observation 80faeb95-ba67-49f6-ab6a-8522578651c5 · outbound

This paper cites Quilt-1m: One million image-text pairs for histopathology, 2023.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Quilt-1m: One million image-text pairs for histopathology, 2023

Reference 20

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Observation 21a8cfea-c031-4cd2-a496-26801bb1d17f · outbound

This paper cites Open- clip, 2021.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Open- clip, 2021

Reference 21

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Observation 15696f4f-05c2-4d6e-b4f0-25e0cb016961 · outbound

This paper cites Attention-based Deep Multiple Instance Learning.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Attention-based Deep Multiple Instance Learning

Reference 22

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Observation b45d62d5-a49d-40b9-925b-a6e2f82afa10 · outbound

This paper cites From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models

Reference 23

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Observation 04b3c5a2-1a43-4062-9d35-27423fa75704 · outbound

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

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Benchmarking self-supervised learn- ing on diverse pathology datasets

Reference 24

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Observation f44bf9f4-96cc-40cb-a62d-8c45893978c5 · outbound

This paper cites 100,000 histological images of human colorectal cancer and healthy tissue.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology 100,000 histological images of human colorectal cancer and healthy tissue

Reference 25

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Observation 3d26b358-4092-4cff-bde7-9de77ca96b6d · outbound

This paper cites Paip 2019: Liver cancer segmentation challenge.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Paip 2019: Liver cancer segmentation challenge

Reference 26

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Observation 390deaf6-8267-4de5-82ff-e46fe159d556 · outbound

This paper cites Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections

Reference 27

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Observation 705aadbd-463d-4455-b067-2b8fa5e930cd · outbound

This paper cites Robbins and Cotran pathologic basis of disease, pro- fessional edition e-book.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Robbins and Cotran pathologic basis of disease, pro- fessional edition e-book

Reference 28

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Observation e78ba28b-fdc4-46ff-b97d-1c7ec05e02a9 · outbound

This paper cites Dual-stream multiple instance learning network for whole slide image classifica- tion with self-supervised contrastive learning.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Dual-stream multiple instance learning network for whole slide image classifica- tion with self-supervised contrastive learning

Reference 29

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Observation 6121db35-4e0b-4f51-bef8-997e7e2dcd97 · outbound

This paper cites Improved baselines with visual instruction tuning, 2023.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Improved baselines with visual instruction tuning, 2023

Reference 30

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Observation ba169590-186f-4bf8-ac01-ff16b7403da9 · outbound

This paper cites Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

Reference 31

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

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology A visual- language foundation model for computational pathology

Reference 32

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Observation 97a2af27-aec8-45c1-922d-b753448b8a7f · outbound

This paper cites A multimodal gen- erative ai copilot for human pathology.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology A multimodal gen- erative ai copilot for human pathology

Reference 33

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This paper cites Gpt-4 technical report, 2023.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Gpt-4 technical report, 2023

Reference 34

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Observation 65720b53-3ea6-4c8f-ad80-5220db002baf · outbound

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CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Gpt-4v(ision) system card

Reference 35

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

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology DINOv2: Learning Robust Visual Features without Supervision

Reference 36

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This paper cites Learning transferable visual models from natural language supervision, 2021.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Learning transferable visual models from natural language supervision, 2021

Reference 37

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This paper cites Learning transferable visual models from natural language supervi- sion.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Learning transferable visual models from natural language supervi- sion

Reference 38

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This paper cites Ocelot: Overlapped cell on tissue dataset for histopathology.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Ocelot: Overlapped cell on tissue dataset for histopathology

Reference 39

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Observation 0a345d8a-f109-4f4d-a1e3-96854d0a3ebf · outbound

This paper cites Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos

Reference 40

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This paper cites Quilt-llava: Visual instruction tuning by extracting localized narratives from open-source histopathology videos.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Quilt-llava: Visual instruction tuning by extracting localized narratives from open-source histopathology videos

Reference 41

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Observation ece1560c-b43a-4eee-9f66-035336673679 · outbound

This paper cites PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology

Reference 42

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Observation 4b1fd2f2-3918-4138-82ac-2f8c58ea08ea · outbound

This paper cites Going deeper through the gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detec- tion.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Going deeper through the gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detec- tion

Reference 43

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Observation 8e4cc4bb-72bc-44f2-8def-e2e8cb900e4f · outbound

This paper cites Sales, Rafael Molina, and Valery Naranjo.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Sales, Rafael Molina, and Valery Naranjo

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:21:47.274439Z

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Observation 60d2acdf-465e-497b-98c7-11dbefbaf23d · outbound

This paper cites A dataset and a methodology for intraoper- ative computer-aided diagnosis of a metastatic colon cancer in a liver.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology A dataset and a methodology for intraoper- ative computer-aided diagnosis of a metastatic colon cancer in a liver

Reference 45

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This paper cites PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration

Reference 46

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Observation cdb593b5-975e-4bb2-ba34-bd28d850110c · outbound

This paper cites Pathasst: A generative foundation ai assistant towards artificial general intelligence of pathology.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Pathasst: A generative foundation ai assistant towards artificial general intelligence of pathology

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:21:47.252779Z

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CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Gemini: A Family of Highly Capable Multimodal Models

Reference 48

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Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-11T14:21:47.242716Z

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CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Rotation equivariant cnns for digital pathology

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:21:47.232295Z

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CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Virchow: A Million-Slide Digital Pathology Foundation Model

Reference 51

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CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology A petri dish for histopathology image analysis

Reference 52

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Reference 53

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Observation d51e78ee-17d7-46fe-8268-f5d6147f00ce · outbound

This paper cites Predicting axillary lymph node metastasis in early breast cancer using deep learning on primary tumor biopsy slides.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Predicting axillary lymph node metastasis in early breast cancer using deep learning on primary tumor biopsy slides

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:21:47.191772Z

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CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Camel: A weakly supervised learning framework for histopathology image segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:21:47.177122Z

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Observation a3b5219b-168b-492c-a227-34b946e24e61 · outbound

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CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology A whole-slide foundation model for digital pathology from real-world data

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:21:47.163806Z

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Observation 5fb68976-3bc0-4ddd-8421-bfa7c2f692ca · outbound

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CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Qwen2 Technical Report

Reference 57

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CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T14:21:46.668110Z

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CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Transferring the knowledge of vision-language model for pathological image classifi- cation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:21:47.150945Z

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Observation 76349b5b-4ff1-47a7-a03f-25d13aadca6d · outbound

This paper cites BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T14:21:46.676092Z

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Observation 0d5a13eb-53b1-454a-a0ec-b8d65e022ec1 · outbound

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:21:46.680229Z

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Observation bdeea463-2870-4cdc-ab0e-027568c8f867 · outbound

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Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-11T14:21:47.138101Z

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Observation 125c3f46-7b3a-4fdd-90ac-d20af6117f19 · outbound

This paper cites Concentrate solely on the characteristics or diagnosis of the displayed cells or tissues or overall structure.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Concentrate solely on the characteristics or diagnosis of the displayed cells or tissues or overall structure

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:21:47.124297Z

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Observation 79bc7a35-2fda-44ed-99e6-80ab09fbbedd · outbound

This paper cites Avoid including any descriptions that are uncertain.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology Avoid including any descriptions that are uncertain

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:21:47.109997Z

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Observation 5d5a1b9c-daf6-4c37-addb-4196a1dfaaee · outbound

This paper cites You can describe the image in order of positions or include the location after each observation.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology You can describe the image in order of positions or include the location after each observation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:21:47.096973Z

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Observation 92a3e885-3533-43a6-bb42-d2214b04d0a9 · outbound

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Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-11T14:21:47.083690Z

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Observation 195bc869-5673-45c3-8d80-a588c563cd3e · outbound

This paper cites If certainty is not possible, refrain from making a diagnosis.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology If certainty is not possible, refrain from making a diagnosis

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:21:47.068865Z

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Observation eed0dca1-8cdc-4e23-8562-9b42b3a67298 · outbound

This paper cites question.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology question

Reference 68

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Pith citing papers

Observation dfe477eb-f73d-4d41-b61f-78d573640904 · inbound

VLM-based Prompts as the Optimal Assistant for Unpaired Histopathology Virtual Staining cites this paper.

VLM-based Prompts as the Optimal Assistant for Unpaired Histopathology Virtual Staining CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology

Reference 57

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Observation e36dbd80-8242-4f5d-b36d-18cfb67fbf4b · inbound

Discovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoning cites this paper.

Discovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoning CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology

Reference 11

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GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning cites this paper.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:42.848632Z

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Observation b77b9c8c-ef4b-418e-a070-ef356d173462 · inbound

EndoGov: A knowledge-governed multi-agent expert system for endometrial cancer risk stratification cites this paper.

EndoGov: A knowledge-governed multi-agent expert system for endometrial cancer risk stratification CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology

Reference 42

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.