Paper Citation Record · LEDGER

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis

As of 20 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2607.17782.

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

pith.paper-citation-record.v1
2607.17782 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:38:11.952066Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 38cacca3-2fa1-4007-bfec-23bad18aadbb · outbound

This paper cites an unresolved cited work.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:38:12.732661Z

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Observation 27ce466f-8cbe-4553-bf51-be2b6e9ebb12 · outbound

This paper cites Although task -specific supervised models can achieve strong performance, their development depends on labelled datasets that are costly to curate and often limited in size.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Although task -specific supervised models can achieve strong performance, their development depends on labelled datasets that are costly to curate and often limited in size

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.718142Z

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Observation 680cf686-e835-46b4-91b5-1b5d3a52c04b · outbound

This paper cites an unresolved cited work.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Unresolved cited work

Reference 3

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Observation f8039b04-5875-4481-b62e-ba633237acfd · outbound

This paper cites an unresolved cited work.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Unresolved cited work

Reference 4

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Observation 9c67ec06-28ed-471b-8a6d-78f4da122edc · outbound

This paper cites The proposed framework was submitted under the team name Dolphins_creators and achieved second place overall in the Method Track.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis The proposed framework was submitted under the team name Dolphins_creators and achieved second place overall in the Method Track

Reference 5

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Observation 4eb49bd8-9fe6-4827-adfd-f4f5b2c8b1bc · outbound

This paper cites an unresolved cited work.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Unresolved cited work

Reference 6

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Observation 826e9db1-45de-42f9-bdb5-f1905d494f71 · outbound

This paper cites an unresolved cited work.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Unresolved cited work

Reference 7

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Observation 017d2527-6870-4855-b4c4-e80e2b400b3c · outbound

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

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Emerging properties in self -supervised vision transformers

Reference 8

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Observation e5ec359d-ee4b-4a9e-93b4-fd3a7c116cc7 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

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Observation df1dba86-6ec1-4995-b578-4f7e1a154aa2 · outbound

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

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis DINOv2: Learning Robust Visual Features without Supervision

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T15:38:11.847823Z

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Observation 1d70e486-7460-4887-8fa2-1e8192832ccd · outbound

This paper cites Looking into the functional architecture of the brain with diffusion MRI.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Looking into the functional architecture of the brain with diffusion MRI

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.547140Z

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Observation f89f1b86-c8a9-4c23-9e4e-fcd2dbfe38bb · outbound

This paper cites The clinical use of structural MRI in Alzheimer disease.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis The clinical use of structural MRI in Alzheimer disease

Reference 12

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Observation 5e5ba546-d7d0-4d00-adc5-7af328112b78 · outbound

This paper cites A survey on deep learning in medical image analysis.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis A survey on deep learning in medical image analysis

Reference 13

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Observation f14e0d39-9a68-438c-a4ce-3df144d81b84 · outbound

This paper cites Robust and data -efficient generalization of self-supervised machine learning for diagnostic imaging.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Robust and data -efficient generalization of self-supervised machine learning for diagnostic imaging

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.502659Z

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Observation d01c4501-52ed-4427-a6b5-ebb5446a29e4 · outbound

This paper cites Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging

Reference 15

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Observation a6640f36-478a-4650-903c-8e4ae17b4956 · outbound

This paper cites AMAES [17] and related MAE-based approaches focused more specifically on three -dimensional brain MRI segmentation, while BrainAgeNeXt.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis AMAES [17] and related MAE-based approaches focused more specifically on three -dimensional brain MRI segmentation, while BrainAgeNeXt

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.702802Z

Source-reported events for the cited work

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Observation 38509bba-a74b-46cf-89a1-9c9a151e53d4 · outbound

This paper cites Simclr: A simple framework for contrastive learning of visual representations [C].

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Simclr: A simple framework for contrastive learning of visual representations [C]

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.482193Z

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Observation 530a6b88-02bc-4f15-a62a-2d0c1028e42d · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Momentum contrast for unsupervised visual representation learning

Reference 18

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BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Unresolved cited work

Reference 19

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Observation aeb5b015-e7ee-4ec2-96c0-b398cce7ffe1 · outbound

This paper cites U -net: Convolutional networks for biomedical image segmentation.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis U -net: Convolutional networks for biomedical image segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.252952Z

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Observation 2ecf76b1-0ee5-45bb-bfe2-0c1ad0eab453 · outbound

This paper cites Masked autoencoders are scalable vision learners.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Masked autoencoders are scalable vision learners

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.428466Z

Source-reported events for the cited work

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Observation 968a1cba-546a-4337-acd2-ede415e20378 · outbound

This paper cites Models genesis: Generic autodidactic models for 3d medical image analysis.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Models genesis: Generic autodidactic models for 3d medical image analysis

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.402068Z

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Observation bda38db5-3f2c-4ca8-b9ff-81ca691b9d91 · outbound

This paper cites Self -supervised pre -training of swin transformers for 3d medical image analysis.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Self -supervised pre -training of swin transformers for 3d medical image analysis

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.383440Z

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Observation 17e8dc29-f9f2-4aaf-b97e-b662bc14f350 · outbound

This paper cites Towards generalisable foundation models for brain MRI.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Towards generalisable foundation models for brain MRI

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.359905Z

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Observation 2f9d58b3-9ab0-4cba-9648-a4468e7ac1e0 · outbound

This paper cites A generalizable foundation model for analysis of human brain MRI.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis A generalizable foundation model for analysis of human brain MRI

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.336062Z

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Observation ab75d99b-f9bb-4b1c-b023-1ce861690901 · outbound

This paper cites Triad: Vision foundation model for 3d magnetic resonance imaging.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Triad: Vision foundation model for 3d magnetic resonance imaging

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.315454Z

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Observation 53d1e9bc-c507-4b70-a9d0-aac8c34cf896 · outbound

This paper cites AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation

Reference 27

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BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Unresolved cited work

Reference 28

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This paper cites An OpenMind for 3D medical vision self -supervised learning.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis An OpenMind for 3D medical vision self -supervised learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.294310Z

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This paper cites BrainAgeNeXt: advancing brain age modeling for individuals with multiple sclerosis.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis BrainAgeNeXt: advancing brain age modeling for individuals with multiple sclerosis

Reference 30

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Observation d39f85f6-e4c2-485d-8c20-465b6939d238 · outbound

This paper cites nnU -Net: a self-configuring method for deep learning -based biomedical image segmentation.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis nnU -Net: a self-configuring method for deep learning -based biomedical image segmentation

Reference 31

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Observation 7a3ad34b-f4fb-4863-85c6-51decda2f1ba · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Unetr: Transformers for 3d medical image segmentation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.215445Z

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This paper cites xlstm: Extended long short -term memory.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis xlstm: Extended long short -term memory

Reference 33

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Observation 83fdf805-b434-4443-81b4-3172d8165e1c · outbound

This paper cites Decoupled Weight Decay Regularization.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Decoupled Weight Decay Regularization

Reference 34

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Observation a4c7d848-2c5c-4462-a90e-8132e521d9ec · outbound

This paper cites Measures of the amount of ecologic association between species.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Measures of the amount of ecologic association between species

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.175114Z

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Observation 526fe04a-eaee-4115-b58d-a9a2d3232670 · outbound

This paper cites Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 36

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Observation d4a55c0f-e34c-452c-b261-50933f9bfbd3 · outbound

This paper cites Foundation Model for Medical Imaging: A Comprehensive Review.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Foundation Model for Medical Imaging: A Comprehensive Review

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.155115Z

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Observation 769e19cb-6ac7-43ab-acd8-b3f0f56ba7cd · outbound

This paper cites Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge

Reference 38

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Observation d1ca2169-9860-4e18-a5f6-cc9052206e6a · outbound

This paper cites Foundation Models for Medical Imaging (FOMO) 2025 Challenge: Official Results.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Foundation Models for Medical Imaging (FOMO) 2025 Challenge: Official Results

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:38:12.138712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.