Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-22T07:20:10.061711Z
Paper Citation Record · LEDGER
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation
As of 7 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2605.21835.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
26 of 26 outbound references displayed
- verified exact4
- verified fuzzy22
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation ca1c8491-701a-4003-8d99-7cf440eda20f · outbound
This paper cites Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body fdg-pet/ct.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body fdg-pet/ct
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.516350Z
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.
Observation 20be4bfe-ffca-4755-978a-6883ee646dd1 · outbound
This paper cites Snmmi procedure standard/eanm practice guideline on pediatric 18f-fdg pet/ct for oncology 1.0.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Snmmi procedure standard/eanm practice guideline on pediatric 18f-fdg pet/ct for oncology 1.0
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.503158Z
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.
Observation 39466fec-948e-4c05-928c-b60a2178fe76 · outbound
This paper cites Ai- driven multi-lesion detection in whole-body fdg pet/ct.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Ai- driven multi-lesion detection in whole-body fdg pet/ct
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.513053Z
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.
Observation 2302cb7f-9659-4ef8-99e7-c73864f6088b · outbound
This paper cites Pet/ct based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Pet/ct based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.544333Z
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.
Observation f6f71607-e96e-4450-9f1f-16a5ecbdfc59 · outbound
This paper cites Head and neck tumor segmentation from [18F]F- FDG PET/CT images based on 3D diffusion model.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Head and neck tumor segmentation from [18F]F- FDG PET/CT images based on 3D diffusion model
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.530621Z
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.
Observation e4485c31-d8da-4cfc-9966-6a9d3dff92d7 · outbound
This paper cites Developing a pet/ct foundation model for cross-modal anatomical and functional imaging.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Developing a pet/ct foundation model for cross-modal anatomical and functional imaging
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.565520Z
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.
Observation 38f7c226-2383-4431-8b57-3c9145f9e008 · outbound
This paper cites Delving into pre- training for domain transfer: A broad study of pre-training for domain generalization and domain adaptation: Wi et al.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Delving into pre- training for domain transfer: A broad study of pre-training for domain generalization and domain adaptation: Wi et al
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.555056Z
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.
Observation 6dc99983-bbee-4177-89e3-66836dcc2704 · outbound
This paper cites Act: Semi-supervised domain-adaptive medical image segmentation with asymmetric co-training.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Act: Semi-supervised domain-adaptive medical image segmentation with asymmetric co-training
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.571637Z
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.
Observation 66de0878-79ef-4ecf-9029-77ee6493bc3c · outbound
This paper cites Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T07:21:12.696979Z
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.
Observation f0960a20-1878-4b60-956f-9c0210e5ffa4 · outbound
This paper cites Chest-diffusion: A light-weight text-to-image model for report-to-cxr generation.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Chest-diffusion: A light-weight text-to-image model for report-to-cxr generation
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.533157Z
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.
Observation e875aa20-fd53-4325-a283-5746b500738c · outbound
This paper cites A generalizable foundation model for analysis of human brain mri.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation A generalizable foundation model for analysis of human brain mri
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.509967Z
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.
Observation 36649b11-59d4-46a3-a2f4-1abef245825c · outbound
This paper cites Masked au- toencoders are scalable vision learners.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Masked au- toencoders are scalable vision learners
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.536388Z
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.
Observation e035463f-d4d4-4e7a-b7c9-2817d1ffe201 · outbound
This paper cites A simple framework for contrastive learning of visual representations.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation A simple framework for contrastive learning of visual representations
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.506822Z
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.
Observation 97bb473d-fe2a-4337-99a4-c448bf73e8a7 · outbound
This paper cites Developing a PET/CT Foundation Model for Cross-Modal Anatomical and Functional Imaging.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Developing a PET/CT Foundation Model for Cross-Modal Anatomical and Functional Imaging
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T07:21:12.689963Z
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.
Observation 1f7b7c9c-da96-492c-91b3-ca23d50b4cd9 · outbound
This paper cites A whole- body fdg-pet/ct dataset with manually annotated tumor lesions.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation A whole- body fdg-pet/ct dataset with manually annotated tumor lesions
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.539293Z
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.
Observation f592b3db-3092-43d9-ad01-eaebe9d6d6c6 · outbound
This paper cites A repository of annotated PSMA and FDG PET/CT images for algorithm development in staging of mcrpc for treament with 177Lu-PSMA ther- apy.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation A repository of annotated PSMA and FDG PET/CT images for algorithm development in staging of mcrpc for treament with 177Lu-PSMA ther- apy
Reference 16
- Resolution
- verified exact
- doi, observed 2026-05-22T07:21:12.567637Z
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.
Observation a690b2c6-4844-4685-8b91-fb5717d9c1f0 · outbound
This paper cites Spade (Stanford PET/CT abnormality detection).
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Spade (Stanford PET/CT abnormality detection)
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.551455Z
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.
Observation 4cec9872-d43d-4bab-b9ab-4433da4ea1ed · outbound
This paper cites Toward a vision-language foundation model for medical data: Multimodal dataset and benchmarks for vietnamese pet/ct report generation.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Toward a vision-language foundation model for medical data: Multimodal dataset and benchmarks for vietnamese pet/ct report generation
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.559013Z
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.
Observation 3fac8a57-af82-45f0-9fee-345c52497342 · outbound
This paper cites Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d medical image segmentation.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d medical image segmentation
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.568575Z
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.
Observation 9d08e4a8-c8c4-4b40-97ea-9889b798fad5 · outbound
This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.527304Z
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.
Observation 34c95bb6-042c-4c8a-b16e-c1deac23fb95 · outbound
This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.562415Z
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.
Observation ce4af631-cbc6-4599-9c4a-5912c92a152f · outbound
This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Swin transformer: Hierarchical vision transformer using shifted windows
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.519961Z
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.
Observation c64edc7c-1073-48dd-a720-89a571d816ae · outbound
This paper cites Rethinking evaluation of infrared small target detection.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Rethinking evaluation of infrared small target detection
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.547821Z
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.
Observation 94ac898a-fae3-49b9-9c62-717c95eeb089 · outbound
This paper cites Unimrseg: Unified modality-relax segmentation via hierarchical self-supervised compensation.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Unimrseg: Unified modality-relax segmentation via hierarchical self-supervised compensation
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.523553Z
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.
Observation 1f4e4e52-ebe7-4625-a48b-ec7089175867 · outbound
This paper cites Deep learning-based non-contrast mri model for nasopharyngeal carcinoma diagnosis: an end-to-end gadolinium-free solution.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Deep learning-based non-contrast mri model for nasopharyngeal carcinoma diagnosis: an end-to-end gadolinium-free solution
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T07:21:13.499442Z
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.
Observation 506a630d-b3a8-4a77-9c5b-3bbbfaea9a63 · outbound
This paper cites M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models.
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T07:21:12.683535Z
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.
Pith citing papers
No inbound Pith citation observations are available.