Paper Citation Record · LEDGER

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling

As of 11 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2604.11348.

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

pith.paper-citation-record.v1
2604.11348 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T16:18:35.754758Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

25 of 25 outbound references displayed

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verified fuzzy23
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c8ec4fc8-e7cd-49a9-8733-719dda8e7db3 · outbound

This paper cites Toward robust mammography-based models for breast cancer risk.Science Trans- lational Medicine, 13(578):eaba4373.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Toward robust mammography-based models for breast cancer risk.Science Trans- lational Medicine, 13(578):eaba4373

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:57.011561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 14e1fb93-4aad-48e8-b858-89f95ce27168 · outbound

This paper cites Screening mri in women with a personal history of breast cancer.Journal of the National Cancer Institute, 108(3):djv349.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Screening mri in women with a personal history of breast cancer.Journal of the National Cancer Institute, 108(3):djv349

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.962604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 244c73de-c98d-46cd-bfc6-2719b0486385 · outbound

This paper cites Novel approaches to screening for breast cancer.Radiology, 297(2):266–285.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Novel approaches to screening for breast cancer.Radiology, 297(2):266–285

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:57.001223Z

Source-reported events for the cited work

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Observation f9ec03aa-d210-455b-84f3-08331fa74ac2 · outbound

This paper cites A breast cancer predic- tion model incorporating familial and personal risk factors.Statistics in medicine, 23(7):1111–1130.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling A breast cancer predic- tion model incorporating familial and personal risk factors.Statistics in medicine, 23(7):1111–1130

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.969911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 61a37a30-bbc2-44c5-9060-e4e16da47777 · outbound

This paper cites Predicting short-to long-term breast cancer risk from longitudinal mammographic screening history.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Predicting short-to long-term breast cancer risk from longitudinal mammographic screening history

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.973255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5892e995-5744-4805-a675-b26598ff61a5 · outbound

This paper cites Mammo-age: deep learning estimation of breast age from mammograms.Nature Communications, 16(1):10934.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Mammo-age: deep learning estimation of breast age from mammograms.Nature Communications, 16(1):10934

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.947960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4a6eb73c-f91b-4d7b-b1cd-f599fb8792c3 · outbound

This paper cites Koller, Ani Ambroladze, E.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Koller, Ani Ambroladze, E

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.944183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 01a5d14e-8d7e-4231-9ae4-0991b494ad70 · outbound

This paper cites As- sessing quantitative parenchymal features at baseline dynamic contrast-enhanced mri and cancer occurrence in women with extremely dense breasts.Radiology, 308(2):e222841.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling As- sessing quantitative parenchymal features at baseline dynamic contrast-enhanced mri and cancer occurrence in women with extremely dense breasts.Radiology, 308(2):e222841

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.959189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3446d7b3-a12c-4a67-a5ed-9ea6094ef7e3 · outbound

This paper cites Accurate and efficient fetal birth weight estimation from 3d ultrasound.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Accurate and efficient fetal birth weight estimation from 3d ultrasound

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.932435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ad938a18-4690-4d0f-b65d-89ce1c9a72ce · outbound

This paper cites 2d, 2.5 d, or 3d? comparing dimen- sional approaches in deep neural networks for 3d medical image analysis.Journal of Imaging Informatics in Medicine, pages 1–23.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling 2d, 2.5 d, or 3d? comparing dimen- sional approaches in deep neural networks for 3d medical image analysis.Journal of Imaging Informatics in Medicine, pages 1–23

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.936486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c90565ed-2da1-4305-a91c-be75ce54352e · outbound

This paper cites 2.75 d: Boosting learning by representing 3d medical imaging to 2d features for small data.Biomedical Signal Processing and Control, 84:104858.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling 2.75 d: Boosting learning by representing 3d medical imaging to 2d features for small data.Biomedical Signal Processing and Control, 84:104858

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.955113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation af99d5b8-5cb9-4339-85f8-41ff53741794 · outbound

This paper cites Interpretable 2.5 d network by hierarchical attention and consistency learning for 3d mri classification.Pattern Recognition, 164:111539.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Interpretable 2.5 d network by hierarchical attention and consistency learning for 3d mri classification.Pattern Recognition, 164:111539

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.976543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 11285250-eadc-45db-8b2a-d04191239a87 · outbound

This paper cites Beyond breast density: risk measures for breast cancer in multiple imaging modalities.Radiology, 306(3):e222575.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Beyond breast density: risk measures for breast cancer in multiple imaging modalities.Radiology, 306(3):e222575

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.983400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b2d79ace-7152-42f4-9b2f-475a19740649 · outbound

This paper cites Assessing breast cancer risk by combining ai for lesion detection and mammographic texture.Radiology, 308(2):e230227.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Assessing breast cancer risk by combining ai for lesion detection and mammographic texture.Radiology, 308(2):e230227

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.986821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4898d8a5-896d-435a-a419-a708ae8c0dc3 · outbound

This paper cites Incorporating global- local tissue changes to predict future breast cancer from longitudinal screening mammograms.Medical Image Analysis, page 103990.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Incorporating global- local tissue changes to predict future breast cancer from longitudinal screening mammograms.Medical Image Analysis, page 103990

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:57.008385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f13c4852-5977-4832-bae4-ac3554641b19 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-11T09:01:00.643354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 67ca8ba9-a72d-462f-8662-32ca39a05815 · outbound

This paper cites Attention-based deep multiple instance learning.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Attention-based deep multiple instance learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.997729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5b71ee64-fa36-45e0-be7e-d6af09958030 · outbound

This paper cites Transmil: Transformer based correlated multiple instance learning for whole slide image classification.Advances in neural information processing systems, 34:2136–2147.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Transmil: Transformer based correlated multiple instance learning for whole slide image classification.Advances in neural information processing systems, 34:2136–2147

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.990662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3ef7908c-8b8f-40cf-8db1-2186a123222b · outbound

This paper cites Mambamil: Enhancing long sequence modeling with sequence reordering in computational pathology.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Mambamil: Enhancing long sequence modeling with sequence reordering in computational pathology

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.994453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2416d183-a92f-4772-8003-4b3852b510c6 · outbound

This paper cites In defense of lstms for addressing multiple instance learning problems.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling In defense of lstms for addressing multiple instance learning problems

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:57.004785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d2f1ac07-a2c4-4a9d-8f14-0f2f577fd5f5 · outbound

This paper cites Evaluation of multislice inputs to convolutional neural networks for medical image segmentation.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Evaluation of multislice inputs to convolutional neural networks for medical image segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.979803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e2c2fd93-9e8a-4b89-8d0b-ec9991728401 · outbound

This paper cites Ordinal learning: Longitudinal attention alignment model for predicting time to future breast cancer Screening Breast MRI for Cancer Risk Prediction 11 events from mammograms.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Ordinal learning: Longitudinal attention alignment model for predicting time to future breast cancer Screening Breast MRI for Cancer Risk Prediction 11 events from mammograms

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.966005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a34f6245-6ef1-4aba-88fa-d596d17f20a5 · outbound

This paper cites On the c-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data.Statistics in medicine, 30(10):1105–1117.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling On the c-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data.Statistics in medicine, 30(10):1105–1117

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.940220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 44c8db19-6aa7-4564-98a9-fdfe833df6fb · outbound

This paper cites An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapyresponseinwomenwithbreastcancer.Nature communications,15(1):9613.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapyresponseinwomenwithbreastcancer.Nature communications,15(1):9613

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:59:56.951458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9c96e0d7-78d6-4f32-83ba-201bf7303d2f · outbound

This paper cites Med3D: Transfer Learning for 3D Medical Image Analysis.

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling Med3D: Transfer Learning for 3D Medical Image Analysis

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:01:00.650010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.