Paper Citation Record · LEDGER

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance

As of 17 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 3 inbound Pith citation observations for arXiv:2505.11997.

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

pith.paper-citation-record.v1
2505.11997 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

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measured 49 of 49 standing notices

One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:13:48.590115Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T17:18:14.737755Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation ad62e13d-cc42-449d-98ce-b8584c61aa8e · outbound

This paper cites Representation learning of histopathology images using graph neural networks.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Representation learning of histopathology images using graph neural networks

Reference 1

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Observation 31b5a157-d588-4f9c-8b37-515bc6fbe2d0 · outbound

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

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis

Reference 7

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Observation 75a4903f-bb0b-49c9-89d4-2d3f2447b689 · outbound

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

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Towards a general-purpose foundation model for computational pathology

Reference 9

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Observation e59aa706-6157-48b4-8fbe-c7471a7a5e52 · outbound

This paper cites Ad- dressing failure prediction by learning model confidence.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Ad- dressing failure prediction by learning model confidence

Reference 10

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Observation aa339353-f0e0-48c2-8a3a-d98851e3b90f · outbound

This paper cites Solving the multiple instance problem with axis-parallel rectangles.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Solving the multiple instance problem with axis-parallel rectangles

Reference 11

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Observation 430f757c-a899-45f6-8fad-e354b5b601f0 · outbound

This paper cites Learning visual features by colorization for slide-consistent survival prediction from whole slide images.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Learning visual features by colorization for slide-consistent survival prediction from whole slide images

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.903818Z

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Observation 149043a3-a249-4904-a640-6836f2ce729c · outbound

This paper cites Hypergraph neural net- works.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Hypergraph neural net- works

Reference 14

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Observation 540a4eae-79e3-4c91-9bdb-022b787653e6 · outbound

This paper cites Scaling self-supervised learning for histopathology with masked image modeling.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Scaling self-supervised learning for histopathology with masked image modeling

Reference 15

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Observation 114f3308-0e62-4078-89a9-cdad672cb325 · outbound

This paper cites Node-aligned graph convolutional network for whole-slide image representation and classification.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Node-aligned graph convolutional network for whole-slide image representation and classification

Reference 16

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Observation 7ce8a9d3-9865-440a-816f-c7b3fdc943ae · outbound

This paper cites Neural networks: a compre- hensive foundation.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Neural networks: a compre- hensive foundation

Reference 17

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Observation 5062ca74-9592-4d38-a61c-f267f922abbf · outbound

This paper cites Hˆ 2-mil: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Hˆ 2-mil: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis

Reference 20

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Observation bc3eebc2-5961-4878-9595-7ced064997ca · outbound

This paper cites Attention-based deep multiple instance learning.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Attention-based deep multiple instance learning

Reference 21

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Observation e84e6af8-6238-43d8-a6d8-4f542bb7331c · outbound

This paper cites Hmil: Hierarchical multi-instance learning for fine- grained whole slide image classification.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Hmil: Hierarchical multi-instance learning for fine- grained whole slide image classification

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.767987Z

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Observation 61e807ee-3c32-478c-934a-92628b06f3f9 · outbound

This paper cites Multi-modal hy- pergraph contrastive learning for medical image segmen- tation.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Multi-modal hy- pergraph contrastive learning for medical image segmen- tation

Reference 24

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Observation 739b5ffc-1d3a-42d4-824a-d0a961a39563 · outbound

This paper cites Self- normalizing neural networks.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Self- normalizing neural networks

Reference 26

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Observation a037bb20-b74c-44da-af3f-83e8ef7bb4ea · outbound

This paper cites High-order correlation-guided slide-level histology retrieval with self-supervised hash- ing.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance High-order correlation-guided slide-level histology retrieval with self-supervised hash- ing

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.680742Z

Source-reported events for the cited work

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Observation 36b086c8-991e-49f1-a038-ee9bed4fb359 · outbound

This paper cites Artificial intelligence for multimodal data integration in oncology.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Artificial intelligence for multimodal data integration in oncology

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.668394Z

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Observation b1527fde-391a-411a-bed4-254950fa7bf9 · outbound

This paper cites Data-efficient and weakly super- vised computational pathology on whole-slide images.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Data-efficient and weakly super- vised computational pathology on whole-slide images

Reference 30

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Observation eccc67bc-0d51-4f0b-9c9a-33a28adffa5e · outbound

This paper cites A visual-language foundation model for computational pathology.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance A visual-language foundation model for computational pathology

Reference 31

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Observation f5c5156f-3829-40d9-94b3-ae53f4eaee19 · outbound

This paper cites Predicting cancer out- comes from histology and genomics using convolutional networks.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Predicting cancer out- comes from histology and genomics using convolutional networks

Reference 32

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Observation 73cface3-60da-4236-8a3f-16369936ac3e · outbound

This paper cites Sparse multi-modal graph transformer with shared-context processing for representation learning of giga-pixel images.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Sparse multi-modal graph transformer with shared-context processing for representation learning of giga-pixel images

Reference 33

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Observation 040cbad1-461b-4b83-a4ea-1a00b5a3ccd1 · outbound

This paper cites Prognostic genome and transcriptome signatures in colorectal cancers.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Prognostic genome and transcriptome signatures in colorectal cancers

Reference 34

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Observation a7a1bb81-64fa-4ad2-a995-8f18c2f89a46 · outbound

This paper cites Boundary-guided learning for gene expression prediction in spatial transcriptomics.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Boundary-guided learning for gene expression prediction in spatial transcriptomics

Reference 35

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Observation adac73f9-ff2a-4d2c-8d2b-42ac1c3fcc2d · outbound

This paper cites Noise in gene expression: origins, consequences, and control.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Noise in gene expression: origins, consequences, and control

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.576322Z

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Observation b273e432-4f0f-4e01-a204-81b8a9a54f14 · outbound

This paper cites Tumor micro- environment interactions guided graph learning for sur- vival analysis of human cancers from whole-slide patho- logical images.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Tumor micro- environment interactions guided graph learning for sur- vival analysis of human cancers from whole-slide patho- logical images

Reference 38

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Observation 7f319f44-d810-473b-8c90-3bdaf6bf3ef9 · outbound

This paper cites Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation

Reference 39

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This paper cites Transformer-based unsupervised con- trastive learning for histopathological image classification.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Transformer-based unsupervised con- trastive learning for histopathological image classification

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.535084Z

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Observation 3c284dfa-d362-40c4-bf74-fea127c6e939 · outbound

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

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Multi- modal optimal transport-based co-attention transformer with global structure consistency for survival prediction

Reference 41

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Observation fec01d5b-e952-4e56-ab47-1dbba0ee7806 · outbound

This paper cites Whole slide images based cancer survival prediction using at- tention guided deep multiple instance learning networks.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Whole slide images based cancer survival prediction using at- tention guided deep multiple instance learning networks

Reference 42

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Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Unresolved cited work

Reference 43

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Observation 606c5e97-136c-4e33-8c50-e6f657690965 · outbound

This paper cites Prototypical information bottlenecking and disentangling for multimodal cancer survival prediction.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Prototypical information bottlenecking and disentangling for multimodal cancer survival prediction

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.476778Z

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Observation 24588990-d480-4b6b-92f0-4574594c9e5f · outbound

This paper cites Histopathological whole slide image analysis using context-based cbir.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Histopathological whole slide image analysis using context-based cbir

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.463734Z

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Observation 1e924a79-3ccb-42a0-b5ea-45e49166db17 · outbound

This paper cites Cross- modal translation and alignment for survival analysis.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Cross- modal translation and alignment for survival analysis

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.448679Z

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Observation 6729a54e-027c-44c4-9483-7cb33759eed7 · outbound

This paper cites Sheaf hypergraph networks.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Sheaf hypergraph networks

Reference 1997

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.916957Z

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Observation 769cf54e-d13f-4445-be06-779647f7ae94 · outbound

This paper cites Deep residual learning for image recog- nition.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Deep residual learning for image recog- nition

Reference 1998

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Observation 73a842b3-9545-474b-a343-f9b784eb39b5 · outbound

This paper cites Transmil: Transformer based correlated multi- ple instance learning for whole slide image classification.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Transmil: Transformer based correlated multi- ple instance learning for whole slide image classification

Reference 2005

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.562283Z

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Observation 4ece9b36-536c-4608-be76-8041ee3ba54d · outbound

This paper cites Patch- based convolutional neural network for whole slide tissue image classification.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Patch- based convolutional neural network for whole slide tissue image classification

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.831822Z

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Observation cfb18776-a8d0-4a34-91c4-d53de482348c · outbound

This paper cites Dual- stream multiple instance learning network for whole slide image classification with self-supervised contrastive learn- ing.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Dual- stream multiple instance learning network for whole slide image classification with self-supervised contrastive learn- ing

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.695707Z

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Observation c9c085b7-4a94-476d-8711-e0d57dcf15ab · outbound

This paper cites Modeling dense multimodal in- teractions between biological pathways and histology for survival prediction.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Modeling dense multimodal in- teractions between biological pathways and histology for survival prediction

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.781337Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5cc33df1-9f49-4c2f-ac05-5bd18ceb6f1e · outbound

This paper cites Predictive dynamic fusion.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Predictive dynamic fusion

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:03.025032Z

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Observation 362e9b57-b1ae-4088-970e-71d6e9d2a15a · outbound

This paper cites Genomics to select treatment for patients with metastatic breast cancer.Nature, 610(7931):343–348,.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Genomics to select treatment for patients with metastatic breast cancer.Nature, 610(7931):343–348,

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:03.050023Z

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Observation f19a8ee4-b51f-44b5-b183-c5dda185bdc4 · outbound

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

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Pan-cancer integrative histology- genomic analysis via multimodal deep learning

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.971294Z

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Observation c19aeb91-bb2c-4fab-8891-56f82d21d061 · outbound

This paper cites Clinical-grade computa- tional pathology using weakly supervised deep learning on whole slide images.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Clinical-grade computa- tional pathology using weakly supervised deep learning on whole slide images

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:03.037449Z

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Observation 05f57e6b-42ad-4940-9275-bc2c1809a18e · outbound

This paper cites Deep learning with multimodal representation for pancancer prognosis prediction.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Deep learning with multimodal representation for pancancer prognosis prediction

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.998054Z

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Observation 4555904a-4fb9-4bb8-a4f4-58f93272f642 · outbound

This paper cites Histopathology whole slide image analysis with heterogeneous graph rep- resentation learning.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Histopathology whole slide image analysis with heterogeneous graph rep- resentation learning

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:03.012581Z

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Observation 86580b71-e372-49f6-96b9-a88381e799cf · outbound

This paper cites Si-mil: Taming deep mil for self-interpretability in gigapixel histopathology.

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance Si-mil: Taming deep mil for self-interpretability in gigapixel histopathology

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:02.727843Z

Source-reported events for the cited work

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Pith citing papers

Observation 0d87edc5-1540-48de-a0ab-6000dc28d7e7 · inbound

Memory-Augmented Incomplete Multimodal Survival Prediction via Cross-Slide and Gene-Attentive Hypergraph Learning cites this paper.

Memory-Augmented Incomplete Multimodal Survival Prediction via Cross-Slide and Gene-Attentive Hypergraph Learning Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T23:13:48.590115Z

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Observation 1c3d6842-c707-4af1-af06-891bb3af9d31 · inbound

FraPPE: Fast and Efficient Preference-based Pure Exploration cites this paper.

FraPPE: Fast and Efficient Preference-based Pure Exploration Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:18:14.740811Z

Source-reported events for the cited work

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Observation 1ba743f1-27f6-445f-9194-8e3464cdbefd · inbound

Together, Then Apart: Balancing Alignment and Distinctiveness for Multimodal Survival Analysis cites this paper.

Together, Then Apart: Balancing Alignment and Distinctiveness for Multimodal Survival Analysis Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance

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