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Paper Citation Record · LEDGER

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment

As of 7 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2608.03247.

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

pith.paper-citation-record.v1
2608.03247 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:41:26.243705Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 96f7caf1-fd12-403f-b3c6-595a24fc47cc · outbound

This paper cites Multimodal Data Integration for Precision Oncology: Challenges and Future Directions.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

Reference 1

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Source-reported events for the cited work

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Observation 58d23bf0-94dc-4404-a036-175a346e420e · outbound

This paper cites Attention-based deep multiple instance learning.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Attention-based deep multiple instance learning

Reference 2

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no resolver link, observed 2026-08-05T22:41:26.130048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e060880f-d4ae-42f9-8584-6abed5a8cd59 · outbound

This paper cites Data-efficient and weakly supervised computational pathology on whole-slide images.Nature biomedical engineering, 5(6):555–570, 2021.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Data-efficient and weakly supervised computational pathology on whole-slide images.Nature biomedical engineering, 5(6):555–570, 2021

Reference 3

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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 b979da6d-ae62-46e5-bb6a-3770f224d623 · 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, 2021.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Transmil: Transformer based correlated multiple instance learning for whole slide image classification.Advances in neural information processing systems, 34:2136–2147, 2021

Reference 4

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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 34ad5a44-80f4-447e-9486-d9abcc9cd389 · outbound

This paper cites Feature re-embedding: Towards foundation model-level performance in computa- tional pathology.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Feature re-embedding: Towards foundation model-level performance in computa- tional pathology

Reference 5

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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 10f48ac4-9844-43a3-9b3c-7920ae626a1f · outbound

This paper cites Multiple instance classification: Review, taxonomy and compara- tive study.Artificial intelligence, 201:81–105, 2013.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Multiple instance classification: Review, taxonomy and compara- tive study.Artificial intelligence, 201:81–105, 2013

Reference 6

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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 aeff5c6b-4d70-4daa-b9ad-baa9514c5f16 · outbound

This paper cites Pan-cancer integrative histology-genomic analysis via multimodal deep learning.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Pan-cancer integrative histology-genomic analysis via multimodal deep learning

Reference 7

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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 d231e891-1126-4d5c-8227-0990550a09b7 · outbound

This paper cites Multimodal co- attention transformer for survival prediction in gigapixel whole slide images.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Multimodal co- attention transformer for survival prediction in gigapixel whole slide images

Reference 8

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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 30c3d587-ecc9-4790-9d98-92ac71995d69 · outbound

This paper cites Multimodal optimal transport-based co-attention transformer with global structure consistency for survival prediction.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Multimodal optimal transport-based co-attention transformer with global structure consistency for survival prediction

Reference 9

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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 c96f6a8b-4b0f-4308-ab71-798d049374bb · outbound

This paper cites Modeling dense multimodal interactions between biological pathways and histology for survival prediction.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Modeling dense multimodal interactions between biological pathways and histology for survival prediction

Reference 10

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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 099da19a-3e55-476f-84c2-17c5768d10c1 · outbound

This paper cites Multimodal Prototyping for cancer survival prediction.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Multimodal Prototyping for cancer survival prediction

Reference 11

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3130dc12-428e-456c-b079-dd6d2bb8f1da · outbound

This paper cites Cohort-individual cooperative learn- ing for multimodal cancer survival analysis.IEEE Transactions on Medical Imag- ing, 44(2):656–667, 2024.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Cohort-individual cooperative learn- ing for multimodal cancer survival analysis.IEEE Transactions on Medical Imag- ing, 44(2):656–667, 2024

Reference 12

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Source-reported events for the cited work

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Observation a6377b08-0d1d-4636-af7c-cd42eb520468 · outbound

This paper cites an unresolved cited work.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Unresolved cited work

Reference 13

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unresolved
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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 65c7231f-4cdb-4529-b1de-69d018a72d06 · outbound

This paper cites Multimodal data fusion for cancer biomarker discovery with deep learning.Nature machine intelli- gence, 5(4):351–362, 2023.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Multimodal data fusion for cancer biomarker discovery with deep learning.Nature machine intelli- gence, 5(4):351–362, 2023

Reference 14

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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 4f53a7db-28d8-4035-a975-c7c3708a1fbb · outbound

This paper cites A two-stage modeling approach for breast cancer survivability prediction.International journal of medical informatics, 149:104438, 2021.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment A two-stage modeling approach for breast cancer survivability prediction.International journal of medical informatics, 149:104438, 2021

Reference 15

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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 890b7faa-6bd6-4109-99ec-71a3623cd4f0 · outbound

This paper cites Multimodal deep learning for cancer prognosis prediction with clinical information prompts integration.npj Digital Medicine, 2025.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Multimodal deep learning for cancer prognosis prediction with clinical information prompts integration.npj Digital Medicine, 2025

Reference 16

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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 91eccabc-8e84-40ae-bf2a-a3d4bd712d60 · outbound

This paper cites Domain-specific language model pretraining for biomedical natural language processing.ACM Transactions on Computing for Healthcare (HEALTH), 3(1):1–23, 2021.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Domain-specific language model pretraining for biomedical natural language processing.ACM Transactions on Computing for Healthcare (HEALTH), 3(1):1–23, 2021

Reference 17

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Source-reported events for the cited work

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Observation babf8c92-d540-4c22-b9ec-eec8cac21cf5 · outbound

This paper cites Towards a general-purpose foundation model for computational pathology.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Towards a general-purpose foundation model for computational pathology

Reference 18

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verified fuzzy
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Source-reported events for the cited work

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Observation 6da788ce-f7f6-4d2d-a5f7-d855979ba304 · outbound

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CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Unresolved cited work

Reference 19

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Source-reported events for the cited work

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Observation f42a4e18-f3a1-4e53-ad84-66dcdb8f7cd1 · outbound

This paper cites Self-normalizing neural networks.Advances in neural information processing sys- tems, 30, 2017.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Self-normalizing neural networks.Advances in neural information processing sys- tems, 30, 2017

Reference 20

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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 97fb1ead-c452-48e8-b080-a7b235e11732 · outbound

This paper cites A kernel two-sample test.The journal of machine learning research, 13(1):723–773, 2012.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment A kernel two-sample test.The journal of machine learning research, 13(1):723–773, 2012

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a14f4bbc-9966-4eb8-b813-ddad309f3efc · outbound

This paper cites Analysis of survival data under the proportional hazards model.International Statistical Review/Revue Internationale de Statistique, pages 45–57, 1975.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Analysis of survival data under the proportional hazards model.International Statistical Review/Revue Internationale de Statistique, pages 45–57, 1975

Reference 22

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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 bc30e920-0318-4ed6-8191-92b06d68ef08 · outbound

This paper cites Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis

Reference 23

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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 5366df30-1f4c-4862-827c-c620844dc239 · outbound

This paper cites Multi-omics deep learning improves fdg pet-ct-based long-term prognostication of breast cancer.npj Precision Oncology, 2026.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Multi-omics deep learning improves fdg pet-ct-based long-term prognostication of breast cancer.npj Precision Oncology, 2026

Reference 24

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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 f370fa09-4790-4607-a686-67e135b3ffd8 · outbound

This paper cites Visualizing data using t-sne.Jour- nal of machine learning research, 9(11), 2008.

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment Visualizing data using t-sne.Jour- nal of machine learning research, 9(11), 2008

Reference 25

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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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Pith citing papers

No inbound Pith citation observations are available.