Paper Citation Record · LEDGER

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model

As of 23 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2509.03267.

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

pith.paper-citation-record.v1
2509.03267 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:04:43.179825Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

14 of 14 outbound references displayed

verified exact0
verified fuzzy9
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fbe4bd1b-c1a9-4152-9bf1-e609d6fe616b · outbound

This paper cites Medical image analysis86, 102789 (2023).

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model Medical image analysis86, 102789 (2023)

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T11:04:42.094624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fcda5fb-57d0-4157-a7d9-70c82d03e955 · outbound

This paper cites MONAI: An open-source framework for deep learning in healthcare.

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model MONAI: An open-source framework for deep learning in healthcare

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T11:04:42.177945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42fb5f32-0b1f-454a-8557-0e04012d37a1 · outbound

This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:04:43.851288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bb99391d-1d04-470d-aecb-7f29603d9bf2 · outbound

This paper cites Nature Biomedical Engineering 5(6), 493–497 (2021).

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model Nature Biomedical Engineering 5(6), 493–497 (2021)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:04:43.833548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 181d3278-3c57-4ef9-aeea-301504cfc580 · outbound

This paper cites arXiv e-prints pp.

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model arXiv e-prints pp

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:04:43.825560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 356d828a-6a6b-4b94-88b2-c90fd7c0fcdf · outbound

This paper cites In: 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model In: 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:04:43.804741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4726d303-0029-442d-8d04-a804fecd509c · outbound

This paper cites Advances in neural information processing systems33, 6840–6851 (2020).

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model Advances in neural information processing systems33, 6840–6851 (2020)

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T11:04:42.675846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4331919-ac27-4611-b76e-3bee9340cb7e · outbound

This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:04:43.664748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation be53cc74-3dcc-4174-976f-bd0b414bfe9e · outbound

This paper cites Scientific Reports13(1), 7303 (2023).

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model Scientific Reports13(1), 7303 (2023)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:04:43.569541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 82bf3d22-4cab-4eb1-b837-86e1b9ed413d · outbound

This paper cites the cancer imaging archive.

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model the cancer imaging archive

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:04:43.481641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c96689eb-dfaa-4deb-9c1e-209c14fb0977 · outbound

This paper cites Denoising Diffusion Implicit Models.

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model Denoising Diffusion Implicit Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T11:04:43.124742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7394f39-bc48-46e1-8db2-cbaaa50cd7f7 · outbound

This paper cites IEEE journal of biomedical and health informatics26(8), 3966–3975 (2022).

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model IEEE journal of biomedical and health informatics26(8), 3966–3975 (2022)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:04:43.403506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1b795733-c6f0-4e7f-9e0a-b473dc10a97c · outbound

This paper cites Advances in neural information processing systems30 (2017).

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model Advances in neural information processing systems30 (2017)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:04:43.350833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 400a856f-4528-4333-8b1d-bb7f7e6b1677 · outbound

This paper cites IEEE Transactions on Medical Imaging (2025).

SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model IEEE Transactions on Medical Imaging (2025)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T11:04:43.179825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.