Paper Citation Record · LEDGER

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications

As of 19 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2508.07165.

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

pith.paper-citation-record.v1
2508.07165 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:24:20.987554Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

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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

33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation b177ef8b-71dd-4483-aaf7-72445289aaca · outbound

This paper cites Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Transactions on Medical Imaging , 37(11):2514–2525, 2018.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Transactions on Medical Imaging , 37(11):2514–2525, 2018

Reference 1

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Observation 831d6d36-10a2-46a4-b388-25266112a1fd · outbound

This paper cites The alzheimer’s d isease neuroimaging initiative.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications The alzheimer’s d isease neuroimaging initiative

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.289096Z

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Observation 09bf0915-9432-4b2c-9788-b249b7721222 · outbound

This paper cites AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation

Reference 3

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Observation 81fe75df-44d5-481f-922f-b03742706628 · outbound

This paper cites A tumour and liver automatic seg- mentation (ATLAS) dataset on contrast-enhanced magnetic resonance imaging for hepatocellular carcinoma.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications A tumour and liver automatic seg- mentation (ATLAS) dataset on contrast-enhanced magnetic resonance imaging for hepatocellular carcinoma

Reference 4

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Observation fba19a8a-b553-4bc8-bfc3-1cd46264970f · outbound

This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 5

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Observation bf6f6a10-e560-476f-a3b1-6d5f1274ced0 · outbound

This paper cites Minimizing estimated risks on unlabele d data: A new formulation for semi- supervised medical image segmentation.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Minimizing estimated risks on unlabele d data: A new formulation for semi- supervised medical image segmentation

Reference 6

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Observation 1ebab144-430d-479d-9c69-8ddaff5f79b1 · outbound

This paper cites Emre Kavur, N.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Emre Kavur, N

Reference 7

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This paper cites A machine learning approach to radiogenomics of breas t cancer: a study of 922 subjects and 529 DCE-MRI features.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications A machine learning approach to radiogenomics of breas t cancer: a study of 922 subjects and 529 DCE-MRI features

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.253098Z

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Observation 76971587-29b2-43ae-923e-98d563d2f2ea · outbound

This paper cites Duke liver dataset: A publicly available liver MRI dataset w ith liver segmentation masks and series labels.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Duke liver dataset: A publicly available liver MRI dataset w ith liver segmentation masks and series labels

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.244687Z

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Observation 65415bf7-8037-489b-86dc-67f6f4d892cc · outbound

This paper cites Emid ec: a database usable for the automatic evaluation of myocardial infarction from delayed-enhancement cardiac MRI.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Emid ec: a database usable for the automatic evaluation of myocardial infarction from delayed-enhancement cardiac MRI

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.235798Z

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Observation fb8d6ec7-1cd0-43c3-8f8e-59a37e997bdc · outbound

This paper cites fastMRI: A pu blicly available raw k-space and dicom dataset of knee images for accelerated mr image reconstruction using machine learning.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications fastMRI: A pu blicly available raw k-space and dicom dataset of knee images for accelerated mr image reconstruction using machine learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.227405Z

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Observation d30ca242-9237-4916-be51-a20fe314ca97 · outbound

This paper cites HaN-Seg: The head and neck organ-at-risk ct and mr segmentation dataset.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications HaN-Seg: The head and neck organ-at-risk ct and mr segmentation dataset

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.218813Z

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Observation 3737525f-eb4a-453f-9014-e2dcf58b8071 · outbound

This paper cites IXI dataset – brai n development.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications IXI dataset – brai n development

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.210766Z

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Observation 261ea21b-dc28-4aa8-b820-1352f5319c86 · outbound

This paper cites SDR-Former: A siamese dual-resolution transformer for liver lesion classiﬁcation using 3d mu lti-phase imaging.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications SDR-Former: A siamese dual-resolution transformer for liver lesion classiﬁcation using 3d mu lti-phase imaging

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.203010Z

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Observation afc6b866-3624-4e89-857d-0a21b624a91e · outbound

This paper cites A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.194636Z

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Observation fcc4c39c-55ae-4e25-b63f-567283b99ace · outbound

This paper cites Multivariate mixture model for myocardial segmentation combining multi-source images.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Multivariate mixture model for myocardial segmentation combining multi-source images

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.185592Z

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Observation 454756d2-390c-42d9-96eb-461c8ff59597 · outbound

This paper cites Deep-learni ng-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Deep-learni ng-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet

Reference 17

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Observation 3bdc038a-b100-4aa0-bf31-b28c3cb99559 · outbound

This paper cites The medical segmentation decathlon.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications The medical segmentation decathlon

Reference 18

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Observation 072816a3-b89b-4d3a-be8b-f53cd5690a5a · outbound

This paper cites The osteoarthriti s initiative.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications The osteoarthriti s initiative

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.157288Z

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Observation 2f9a78d4-31ac-47c9-a1cf-a5952e1a03e2 · outbound

This paper cites Aut omated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the osteoarthritis initiative.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Aut omated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the osteoarthritis initiative

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.149270Z

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Observation 70c3bd90-c5c4-4548-ab1d-dae82c3d93cb · outbound

This paper cites Oasis-3: longitudinal neu- roimaging, clinical, and cognitive dataset for normal aging and Alzheimer dise ase.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Oasis-3: longitudinal neu- roimaging, clinical, and cognitive dataset for normal aging and Alzheimer dise ase

Reference 21

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Observation 209c0f8f-37d6-4ab2-8b87-241c71b33538 · outbound

This paper cites OpenBhB: a large-scale multi-site brain MRI data-set for age pred iction and debiasing.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications OpenBhB: a large-scale multi-site brain MRI data-set for age pred iction and debiasing

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.131706Z

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Observation 1b417074-10af-440e-bbb8-cc3d35cc388d · outbound

This paper cites Large-scale mul ti-center CT and MRI segmentation of pancreas with deep learning.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Large-scale mul ti-center CT and MRI segmentation of pancreas with deep learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.123417Z

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Observation 3ae5bcda-feda-4ef8-8e2b-e1609caedb5f · outbound

This paper cites Art iﬁcial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, conﬁrmatory study.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Art iﬁcial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, conﬁrmatory study

Reference 24

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Observation 838864ff-bb8f-4250-96c3-32ab91259879 · outbound

This paper cites The Parkinson’s progression markers initiative (PPMI)–establishing a PD biomarker c ohort.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications The Parkinson’s progression markers initiative (PPMI)–establishing a PD biomarker c ohort

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.105557Z

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Observation fb3b793c-ddae-439d-9b17-2e41fcd3b9a1 · outbound

This paper cites E valuation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications E valuation of prostate segmentation algorithms for MRI: the PROMISE12 challenge

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.096096Z

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Observation 1b520029-3d5a-47da-969b-6608f4550ce0 · outbound

This paper cites Prostate158-an expert-annotated 3T mri dataset and algorithm for prostate cancer detection.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Prostate158-an expert-annotated 3T mri dataset and algorithm for prostate cancer detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.086385Z

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This paper cites SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation

Reference 28

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This paper cites Lumbar spine segmentation in MR images: a dataset and a public benchmark.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Lumbar spine segmentation in MR images: a dataset and a public benchmark

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.076264Z

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Observation 0136f3a6-3c28-412e-a52b-f7d680cfed93 · outbound

This paper cites TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI

Reference 30

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Observation 3a02f7dd-9c46-42d8-a1b1-fa4c8f0242d8 · outbound

This paper cites Learning co-plane attention across MRI sequences for diagnosin g twelve types of knee abnormalities.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Learning co-plane attention across MRI sequences for diagnosin g twelve types of knee abnormalities

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.066261Z

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Observation 168f46ca-22b6-4598-b177-2ea30095b50e · outbound

This paper cites Enhancing MRI -based classiﬁcation of Alzheimer’s disease with explainable 3D hybrid compact convolutional t ransformers.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Enhancing MRI -based classiﬁcation of Alzheimer’s disease with explainable 3D hybrid compact convolutional t ransformers

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.056625Z

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Observation 1cb18aaf-8a2b-4ee7-8e22-6174054418ec · outbound

This paper cites Knee osteoarthritis classiﬁ cation using 3D CNN and MRI.

Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications Knee osteoarthritis classiﬁ cation using 3D CNN and MRI

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:24:21.046591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

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