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Paper Citation Record · LEDGER

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation

As of 7 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2508.17524.

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

pith.paper-citation-record.v1
2508.17524 v1

Coverage vector

measured 46 of 46 reference resolution

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measured 47 of 47 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: arxiv_reference, observed 2026-05-21T08:09:51.798631Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation f842f85f-6c3e-41a1-ad6e-2fc1d43027c8 · outbound

This paper cites MRI from Picture to Proton.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation MRI from Picture to Proton

Reference 1

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Observation 1689a244-2b7e-4736-b261-f396f4a69095 · outbound

This paper cites Sparse mri: The application of compressed sensing for rapid mr imaging.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Sparse mri: The application of compressed sensing for rapid mr imaging

Reference 2

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Observation 2842031f-b17d-42e1-a60d-94ba87789c30 · outbound

This paper cites Statistical shape models for 3d medical image segmentation: a review.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Statistical shape models for 3d medical image segmentation: a review

Reference 3

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Observation 8bf41db3-62e3-4d9d-809b-8cf2ee4392cb · outbound

This paper cites Radlex: a new method for indexing online educational materials, 2006.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Radlex: a new method for indexing online educational materials, 2006

Reference 4

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Observation f61126f5-93ac-469b-b637-14eb1533e7ef · outbound

This paper cites Toward best practices in radiology reporting.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Toward best practices in radiology reporting

Reference 5

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Observation fb46fb66-f005-4b86-a887-ff4e2d9a7aff · outbound

This paper cites Deep learning.nature, 521(7553):436– 444, 2015.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Deep learning.nature, 521(7553):436– 444, 2015

Reference 6

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Observation 96b4eb8f-a9c1-4cce-91e4-5b5d6dbe0d6a · outbound

This paper cites Advancing mri reconstruction: a systematic review of deep learning and compressed sensing integration.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Advancing mri reconstruction: a systematic review of deep learning and compressed sensing integration

Reference 7

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Observation ceb27cb4-5fcd-466c-a090-68f4c5bfbb2a · outbound

This paper cites Deep-learning methods for parallel magnetic reso- nance imaging reconstruction: A survey of the current approaches, trends, and issues.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Deep-learning methods for parallel magnetic reso- nance imaging reconstruction: A survey of the current approaches, trends, and issues

Reference 8

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This paper cites A review on deep learning mri reconstruction without fully sampled k-space.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation A review on deep learning mri reconstruction without fully sampled k-space

Reference 9

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Observation 05f37a46-1333-4b9a-96cc-0ec6197b1c22 · outbound

This paper cites A review of deep learning for brain tumor analysis in mri.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation A review of deep learning for brain tumor analysis in mri

Reference 10

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This paper cites Deep learning for brain mri segmentation: state of the art and future directions.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Deep learning for brain mri segmentation: state of the art and future directions

Reference 11

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Observation 61f83ecd-bb65-42c9-b902-c576b360ec50 · outbound

This paper cites Deep semantic segmentation of natural and medical images: a review.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Deep semantic segmentation of natural and medical images: a review

Reference 12

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This paper cites Deep learning approaches for brain tumor detection and classification using mri images (2020 to 2024): a systematic review.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Deep learning approaches for brain tumor detection and classification using mri images (2020 to 2024): a systematic review

Reference 13

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Observation cda99499-728a-48c2-9029-0068df9e9da5 · outbound

This paper cites Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review

Reference 14

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Observation bc6887ec-5d89-4383-a02b-3dbeb4793d6f · outbound

This paper cites Machine-learning-based disease diagnosis: A comprehensive review.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Machine-learning-based disease diagnosis: A comprehensive review

Reference 15

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This paper cites Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on mri.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on mri

Reference 16

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Observation 2983eda3-3f3a-4e98-9efa-e265479b6876 · outbound

This paper cites Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review

Reference 17

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Observation e1900a2f-839a-4e3b-82d6-bdcfd9701a36 · outbound

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

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation On the Opportunities and Risks of Foundation Models

Reference 18

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This paper cites Momentum con- trast for unsupervised visual representation learning.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Momentum con- trast for unsupervised visual representation learning

Reference 19

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This paper cites A simple frame- work for contrastive learning of visual representations.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation A simple frame- work for contrastive learning of visual representations

Reference 20

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This paper cites Emerging properties in self-supervised vision transform- ers.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Emerging properties in self-supervised vision transform- ers

Reference 21

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This paper cites Masked autoencoders are scalable vision learners.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Masked autoencoders are scalable vision learners

Reference 22

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This paper cites iBOT: Image BERT Pre-Training with Online Tokenizer.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 23

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OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 24

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OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Lan- guage models are few-shot learners

Reference 25

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OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation LLaMA: Open and Efficient Foundation Language Models

Reference 26

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OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Attention is all you need

Reference 27

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OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Learning transferable visual models from natural language supervision

Reference 28

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OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation BLIP: Bootstrapping language- image pre-training for unified vision-language understanding and generation

Reference 29

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OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Flamingo: a Visual Language Model for Few-Shot Learning

Reference 30

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OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Visual instruction tuning

Reference 31

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OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Stolte, Yunchao Yang, Kang Liu, Kyle B

Reference 32

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OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Sam3d: Segment anything model in volumetric medical images

Reference 33

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OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Few-shot adap- tation of training-free foundation model for 3d medical image segmentation, 2025

Reference 34

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Observation 940e849a-8d4e-424b-9ccf-d0315a6e717d · outbound

This paper cites Vista3d: A unified segmentation foundation model for 3d medical imaging.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Vista3d: A unified segmentation foundation model for 3d medical imaging

Reference 35

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 19fa9ff2-fe2d-4973-b589-2d98343be84a · outbound

This paper cites Magpie: Alignment data synthesis from scratch by prompting aligned llms with nothing, 2024.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Magpie: Alignment data synthesis from scratch by prompting aligned llms with nothing, 2024

Reference 36

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1b0e0378-17c4-45d7-a02b-e7ecd45efdc1 · outbound

This paper cites Qwen3 technical report, 2025.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Qwen3 technical report, 2025

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T16:57:05.098273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 614b405b-f99d-42e9-b61f-61662cf8cd91 · outbound

This paper cites Janus-pro: Unified multimodal understanding and generation with data and model scaling, 2025.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Janus-pro: Unified multimodal understanding and generation with data and model scaling, 2025

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-05T16:57:05.243692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2f7dfecb-2b6d-426a-ba02-37c20d05e91b · outbound

This paper cites Blip3- o: A family of fully open unified multimodal models-architecture, training and dataset, 2025.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Blip3- o: A family of fully open unified multimodal models-architecture, training and dataset, 2025

Reference 39

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 720bbe46-d997-4365-a6af-a4c489df47c2 · outbound

This paper cites Emerging properties in unified multimodal pretraining, 2025.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Emerging properties in unified multimodal pretraining, 2025

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T16:57:05.394759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:57:05.394759Z digest=sha256:d4d4c1211b84f8f855ff3d37b4f05bb2a714b88bb4e71167820a3dcc493cbe93

Observation fce8c7b6-729e-4023-b75e-74d2f8c8a2f1 · outbound

This paper cites Ming-omni: A unified multimodal model for perception and generation, 2025.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Ming-omni: A unified multimodal model for perception and generation, 2025

Reference 41

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 189f12cd-925a-4a52-b54d-05e7910bceff · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 42

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 31edd8dd-a693-48ee-87af-3af881d8b098 · outbound

This paper cites Diffu- sion models in vision: A survey.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Diffu- sion models in vision: A survey

Reference 43

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation bc1620a1-9459-4e1e-b494-1bbd7ded5afb · outbound

This paper cites Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T16:57:07.350592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T16:57:05.714760Z digest=sha256:fa9159eae4344b3fb6e5a721f05f6336077aad25f431107ed6db611ad7b9b364

Observation 1af399df-c98b-48f0-a7f5-af7e34b68903 · outbound

This paper cites Roth, and Daguang Xu.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Roth, and Daguang Xu

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T16:57:07.182803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T16:57:05.865618Z digest=sha256:263fda5a56b5233a9140dfcef5a5e3d9a15c5d36a0b5d424da2295953de8390c

Observation 8cce73b2-a748-4652-aeda-f947aab8bf87 · outbound

This paper cites Simmim: A simple framework for masked image modeling.

OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation Simmim: A simple framework for masked image modeling

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T16:57:06.814754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T16:57:06.018071Z digest=sha256:90a5c16ffda7f8b1b89f775cea67b2e583aff40b16779a766a1227852596130f

Pith citing papers

Observation 8d3f548a-2190-48ee-8368-28ee4a48f89c · inbound

Regulating Anatomy-Aware Rewards via Trajectory-Integral Feedback for Volumetric Computed Tomography Analysis cites this paper.

Regulating Anatomy-Aware Rewards via Trajectory-Integral Feedback for Volumetric Computed Tomography Analysis OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-21T08:09:51.800276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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