Paper Citation Record · LEDGER

FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.01264.

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

pith.paper-citation-record.v1
2406.01264 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:48:19.513971Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:49:58.525047Z

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 256abd2b-6593-42c9-ab14-effe10772f6a · inbound

MRGen: Segmentation Data Engine for Underrepresented MRI Modalities cites this paper.

MRGen: Segmentation Data Engine for Underrepresented MRI Modalities FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T22:28:07.836605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb9a3872-afae-4a67-bedf-302a73c0ee2f · inbound

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models cites this paper.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T00:48:19.513971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b24b051-6213-45e7-ac68-700f3f158354 · inbound

Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation cites this paper.

Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T17:33:44.127707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a2a2c7e-589b-42fb-87ad-6c7edc5e906d · inbound

Language Models as Higher-Order Planning Formalizers cites this paper.

Language Models as Higher-Order Planning Formalizers FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-13T19:19:56.267003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c03ca70-64de-435b-88ea-824bdb76c4fc · inbound

Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generation cites this paper.

Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generation FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T22:52:45.371490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 035a9a76-5ea3-4d2b-bd8f-249c27ffbc96 · inbound

BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases cites this paper.

BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:49:58.526351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.