Paper Citation Record · LEDGER

Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2406.19611.

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

pith.paper-citation-record.v1
2406.19611 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:10:45.846899Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T06:09:41.688651Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 02c95a41-181b-4c89-9692-be4986831fe4 · inbound

Weakly-Supervised Multimodal Learning on MIMIC-CXR cites this paper.

Weakly-Supervised Multimodal Learning on MIMIC-CXR Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T19:46:10.929225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d766529-bb35-472a-96ba-bde3858e1c45 · inbound

Continually Evolved Multimodal Foundation Models for Cancer Prognosis cites this paper.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T00:29:01.981215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6db728dd-8533-4255-a265-0624e9a2ee7e · inbound

DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery cites this paper.

DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T20:10:45.846899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8376e46b-1551-4512-935c-81fcda67ab3d · inbound

From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research cites this paper.

From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:12:05.158852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 73d77b8c-1683-4c76-b8aa-a3128775a84c · inbound

No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation cites this paper.

No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T16:15:39.917703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edb97b9b-d920-49a3-be5e-7b18f38a56a1 · inbound

HDMoE: A Hierarchical Decoupling-Fusion Mixture-of-Experts Framework for Multimodal Cancer Survival Prediction cites this paper.

HDMoE: A Hierarchical Decoupling-Fusion Mixture-of-Experts Framework for Multimodal Cancer Survival Prediction Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:09:41.691108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 78345f6f-d6cd-421c-ab56-d2764cb54fa4 · inbound

Enhancing Personalized Bladder Cancer Treatment Through Reinforcement Learning: A Recurrent Patient State Transition Decision Support Framework cites this paper.

Enhancing Personalized Bladder Cancer Treatment Through Reinforcement Learning: A Recurrent Patient State Transition Decision Support Framework Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T19:37:05.378308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

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

Resolution: unresolved; no resolver link, observed 2026-08-05T22:41:26.125032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.