Paper Citation Record · LEDGER

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses

As of 15 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2509.10620.

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

pith.paper-citation-record.v1
2509.10620 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:48:32.067219Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:13:16.573507Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T00:13:17.089828Z

Reference resolution

40 of 40 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6fea140f-0915-48d5-8617-25f14c11137b · outbound

This paper cites The stroke outcome optimization project: Acute ischemic strokes from a comprehensive stroke center.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses The stroke outcome optimization project: Acute ischemic strokes from a comprehensive stroke center

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:29.182095Z

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Observation 96554574-fe9f-4496-a516-020779211974 · outbound

This paper cites Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:29.263171Z

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Observation 0ded1747-0468-463a-816b-a229966f02db · outbound

This paper cites Avants, C.L.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Avants, C.L

Reference 3

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Observation 7969917e-e524-4f78-8e69-55554f61c02f · outbound

This paper cites Big Self-Supervised Models Advance Medical Image Classifica- tion.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Big Self-Supervised Models Advance Medical Image Classifica- tion

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:29.372268Z

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Observation b678e329-f441-4c51-85ca-b619951faeeb · outbound

This paper cites Greve, Oula Puonti, Axel Thielscher, Koen Van Leemput, Bruce Fischl, Adrian V.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Greve, Oula Puonti, Axel Thielscher, Koen Van Leemput, Bruce Fischl, Adrian V

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:29.432209Z

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Observation 7e9fd7e9-c618-4551-96b5-75e539f43977 · outbound

This paper cites MONAI: An open-source framework for deep learning in healthcare.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses MONAI: An open-source framework for deep learning in healthcare

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:29.499946Z

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Observation 90dbdd8f-c2dc-4461-9037-e0f632200f67 · outbound

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

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Emerg- ing properties in self-supervised vision transformers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:29.578399Z

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Observation 38bf78e9-f1d0-4eb8-bc83-a60e399f1624 · outbound

This paper cites Tsaftaris.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Tsaftaris

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:29.644234Z

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Observation 887a7f81-f2e5-44b1-bdea-666ca867cc9c · outbound

This paper cites Medical image foundation models in assisting diagnosis of brain tumors: a pilot study.European Radiology, 34, 2024.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Medical image foundation models in assisting diagnosis of brain tumors: a pilot study.European Radiology, 34, 2024

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:29.728240Z

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Observation 116a64d3-d6d5-4222-bdc9-2d238d90b013 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses A simple framework for contrastive learning of visual representations

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:29.790515Z

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Observation 38a2a736-04c9-4b66-bc78-78f835891da8 · outbound

This paper cites Cole, Rudra P.K.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Cole, Rudra P.K

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:29.847739Z

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Observation 953810b7-1a27-4c75-ab4f-c3037ac64012 · outbound

This paper cites Stolte, Yunchao Yang, Kang Liu, Kyle B.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Stolte, Yunchao Yang, Kang Liu, Kyle B

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:29.907561Z

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Observation 1dc8a146-c795-4c86-9138-c2e599c3ab96 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:29.990112Z

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Observation 0aba127f-eb9e-4c7c-b469-8fcd5783611d · outbound

This paper cites Con- trastive learning with continuous proxy meta-data for 3d mri classification.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Con- trastive learning with continuous proxy meta-data for 3d mri classification

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.057663Z

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Observation 41bc1ddf-491e-4171-bb7a-0abf64ec4be1 · outbound

This paper cites an unresolved cited work.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.109961Z

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Observation cf42a7b1-646e-4693-9113-91f89f1da6a1 · outbound

This paper cites Brain tumor seg- mentation with deep neural networks.Medical Image Anal- ysis, 35:18–31, 2017.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Brain tumor seg- mentation with deep neural networks.Medical Image Anal- ysis, 35:18–31, 2017

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.199044Z

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Observation 24648da2-6bb4-4fa3-8cfe-4a24131161a1 · outbound

This paper cites Deep residual learning for image recognition.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Deep residual learning for image recognition

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.271775Z

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Observation edbbad29-91e2-40db-89b6-828b39e9b8f2 · outbound

This paper cites Momentum contrast for unsupervised visual repre- sentation learning.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Momentum contrast for unsupervised visual repre- sentation learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.340725Z

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Observation be6a6bea-5ff6-40e2-8466-65a58a21312b · outbound

This paper cites Masked autoencoders are scal- able vision learners.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Masked autoencoders are scal- able vision learners

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.362622Z

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Observation 76b11277-26b4-44f1-9e83-44ca5e33bb1b · outbound

This paper cites Brain genomics superstruct project initial data release with structural, functional, and behavioral mea- sures.Scientific data, 2(1):1–16, 2015.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Brain genomics superstruct project initial data release with structural, functional, and behavioral mea- sures.Scientific data, 2(1):1–16, 2015

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.430657Z

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Observation 3460472e-110a-4663-b61f-a1ee0c2e43a9 · outbound

This paper cites Mora, Adrian V.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Mora, Adrian V

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.545749Z

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Observation c5e55893-1fc5-43e3-acd5-d5a5722484cd · outbound

This paper cites Information extraction from im- ages (ixi) dataset, 2025.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Information extraction from im- ages (ixi) dataset, 2025

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.658969Z

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Observation c5bb7280-3d88-4f88-bd39-ef0c28b8755c · outbound

This paper cites An approach to building foundation models for brain image analysis.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses An approach to building foundation models for brain image analysis

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.735868Z

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Observation cb4d77df-808a-42e9-b854-185f512b8e15 · outbound

This paper cites Residual and plain convolutional neural networks for 3d brain mri classification.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Residual and plain convolutional neural networks for 3d brain mri classification

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.819352Z

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Observation 56a5fc5b-f607-416c-83a2-5d67fae46777 · outbound

This paper cites Segment anything in medical images.Nature Communications, 15:654, 2024.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Segment anything in medical images.Nature Communications, 15:654, 2024

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.871457Z

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Observation 901327aa-b2f0-430b-a350-61db943ac147 · outbound

This paper cites The parkinson’s progres- sion markers initiative (ppmi)–establishing a pd biomarker cohort.Annals of clinical and translational neurology, 5(12): 1460–1477, 2018.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses The parkinson’s progres- sion markers initiative (ppmi)–establishing a pd biomarker cohort.Annals of clinical and translational neurology, 5(12): 1460–1477, 2018

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:30.938089Z

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Observation 7373abad-26a2-455c-b419-d2b99ddc29f6 · outbound

This paper cites an unresolved cited work.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:31.022156Z

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Observation 18eae971-f6bc-4cb6-b0e5-99c758cc56ea · outbound

This paper cites The dal- las lifespan brain study: A comprehensive adult lifespan data set of brain and cognitive aging.Scientific Data, 12(1):1–14,.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses The dal- las lifespan brain study: A comprehensive adult lifespan data set of brain and cognitive aging.Scientific Data, 12(1):1–14,

Reference 28

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Observation e3ba656b-6c47-4682-84c5-996c36c22c5d · outbound

This paper cites Castro, Anton Schwaighofer, Matthew P.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Castro, Anton Schwaighofer, Matthew P

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:31.142126Z

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Observation 11dad4b3-accb-4cbb-9bef-8c4e26e72da8 · outbound

This paper cites Alzheimer’s disease neuroimaging initiative (adni) clinical characterization.Neurology, 74(3): 201–209, 2010.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Alzheimer’s disease neuroimaging initiative (adni) clinical characterization.Neurology, 74(3): 201–209, 2010

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:31.190965Z

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Observation 13fde1d3-b587-4048-b40e-4f91e068c2c8 · outbound

This paper cites The mayo clinic study of aging: design and sampling, participation, baseline measures and sample characteristics.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses The mayo clinic study of aging: design and sampling, participation, baseline measures and sample characteristics

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:31.294647Z

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Observation 5b801c6a-e1bc-4ae4-8907-c54677e989fb · outbound

This paper cites Rosen and the FTLDNI Study Group.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Rosen and the FTLDNI Study Group

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:31.353012Z

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Observation 3ab2523d-c09f-4037-93a6-ef44eaab32ce · outbound

This paper cites Shinohara, Elizabeth M.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Shinohara, Elizabeth M

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:31.404693Z

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Observation c553a892-26a7-40b5-bd0e-f5af3ee273a9 · outbound

This paper cites The amsterdam open mri collection, a set of multimodal mri datasets for individual difference analyses.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses The amsterdam open mri collection, a set of multimodal mri datasets for individual difference analyses

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:31.479606Z

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Observation e3664497-ac87-4c2c-b784-903205ddc2fc · outbound

This paper cites Revisiting unreasonable effectiveness of data in deep learning era.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Revisiting unreasonable effectiveness of data in deep learning era

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:31.602256Z

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Observation 965960db-c375-4c2f-ac0b-4a4082c55e11 · outbound

This paper cites Tustison, Brian B.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Tustison, Brian B

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:31.643450Z

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Observation 44f01517-8685-4de1-828e-612dd4b7598d · outbound

This paper cites Structural and functional brain scans from the cross-sectional southwest university adult lifespan dataset.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Structural and functional brain scans from the cross-sectional southwest university adult lifespan dataset

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:31.755538Z

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Observation f63b9451-40c5-4950-a051-79cb68bb183f · outbound

This paper cites an unresolved cited work.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Unresolved cited work

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:31.836005Z

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Observation 546d8901-38c6-4cc7-8ca9-62108df00ff3 · outbound

This paper cites Matthews, Chuyang Ye, and Wenjia Bai.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses Matthews, Chuyang Ye, and Wenjia Bai

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:31.958472Z

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Observation 33fda0b2-f85c-4ed7-a1af-b924932038fe · outbound

This paper cites An open science resource for establishing reliability and reproducibility in functional connectomics.Scientific data, 1(1):1–13, 2014.

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses An open science resource for establishing reliability and reproducibility in functional connectomics.Scientific data, 1(1):1–13, 2014

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:32.067219Z

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Pith citing papers

Observation 30cdf557-135f-46f4-aea2-46f9b1d996ac · inbound

A continually expandable foundation model for brain MRI cites this paper.

A continually expandable foundation model for brain MRI Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-12T00:13:17.094353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.