Paper Citation Record · LEDGER

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets

As of 20 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2608.11076.

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

pith.paper-citation-record.v1
2608.11076 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:52:26.571145Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

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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

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation 9c70ccc7-98c4-49b6-b292-1a79c359f0b4 · outbound

This paper cites Role of psma pet-guided metastases-directed therapy in oligometastatic recurrent prostate cancer.Frontiers in oncology, 12:929444, 2022.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Role of psma pet-guided metastases-directed therapy in oligometastatic recurrent prostate cancer.Frontiers in oncology, 12:929444, 2022

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.908023Z

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Observation 876c904d-7222-43bb-b1e5-cf9b64e1688f · outbound

This paper cites Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T10:52:26.481418Z

Source-reported events for the cited work

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Observation f8383091-4cc3-49bd-8d1d-24483f96e51b · outbound

This paper cites Classification, staging and prognosis of lung cancer.European journal of radiology, 45(1):8–17, 2003.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Classification, staging and prognosis of lung cancer.European journal of radiology, 45(1):8–17, 2003

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.901551Z

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 45a1b6db-4762-41a1-9ba0-c62170556e98 · outbound

This paper cites Applications of artificial intelligence in psma pet/ct for prostate cancer imaging.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Applications of artificial intelligence in psma pet/ct for prostate cancer imaging

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.893741Z

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 1628c7af-77d1-4662-a919-cb99130d3037 · outbound

This paper cites Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning.Medical physics, 52(1):300–320, 2025.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning.Medical physics, 52(1):300–320, 2025

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.886474Z

Source-reported events for the cited work

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Observation 00525f3f-90a9-40aa-b4aa-645d2dbe6faa · outbound

This paper cites Active learning on medical image.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Active learning on medical image

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.879363Z

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 3d8ef57b-9d92-4851-b477-02041072b3c7 · outbound

This paper cites Bone metastases.Abeloff’s clinical oncology, pages 809– 830, 2020.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Bone metastases.Abeloff’s clinical oncology, pages 809– 830, 2020

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.871823Z

Source-reported events for the cited work

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Observation f7857d3f-3b6b-48cf-8c2c-a9c6a9de1db0 · outbound

This paper cites Clini- copathological insights and management of liver metastases: Current advances and future perspectives.World Journal of Hepatology, 17(10):110026, 2025.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Clini- copathological insights and management of liver metastases: Current advances and future perspectives.World Journal of Hepatology, 17(10):110026, 2025

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.864324Z

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 febabdd3-7dcc-43d0-80eb-ac87284687f8 · outbound

This paper cites Results from the autopet challenge on fully automated lesion segmentation in oncologic pet/ct imaging.Nature Machine Intelligence, 6(11):1396–1405, 2024.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Results from the autopet challenge on fully automated lesion segmentation in oncologic pet/ct imaging.Nature Machine Intelligence, 6(11):1396–1405, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.857281Z

Source-reported events for the cited work

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Observation 48617300-2ddf-46b7-bb05-5f5c6e4b6331 · outbound

This paper cites A whole-body fdg-pet/ct dataset with manually annotated tumor lesions.Scientific Data, 9(1):601, 2022.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets A whole-body fdg-pet/ct dataset with manually annotated tumor lesions.Scientific Data, 9(1):601, 2022

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.850259Z

Source-reported events for the cited work

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Observation 23ab68b0-fc15-4df8-b11a-a006b15c6e68 · outbound

This paper cites an unresolved cited work.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:52:26.844113Z

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Observation 8d70840e-2d22-431f-baac-5a5d8071162c · outbound

This paper cites Deep semi-supervised learning for medical image segmentation: A review.Expert Systems with Applications, 245:123052, 2024.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Deep semi-supervised learning for medical image segmentation: A review.Expert Systems with Applications, 245:123052, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.838033Z

Source-reported events for the cited work

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Observation fc87600e-dd6c-4758-989e-d38a4179cb90 · outbound

This paper cites How we read oncologic fdg pet/ct.Cancer Imaging, 16(1):35, 2016.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets How we read oncologic fdg pet/ct.Cancer Imaging, 16(1):35, 2016

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.831252Z

Source-reported events for the cited work

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Observation acf3b212-d723-484e-8b5e-1c9275bc8aff · outbound

This paper cites C2maot: Cross-modal complementary masked autoencoder with optimal transport for cancer segmentation in pet-ct images.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets C2maot: Cross-modal complementary masked autoencoder with optimal transport for cancer segmentation in pet-ct images

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.822981Z

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 6875bddb-512e-48c8-a275-c9a0d83ff12f · outbound

This paper cites Au- tomated lesion segmentation in whole-body pet/ct - multitracer multicenter generalization.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Au- tomated lesion segmentation in whole-body pet/ct - multitracer multicenter generalization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.814994Z

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Observation 893f0393-7a06-486a-93c9-f31891790be0 · outbound

This paper cites nnu-net: a self- configuring method for deep learning-based biomedical image segmentation.Nature methods, 18(2):203–211, 2021.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets nnu-net: a self- configuring method for deep learning-based biomedical image segmentation.Nature methods, 18(2):203–211, 2021

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.807019Z

Source-reported events for the cited work

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Observation 06134266-5f9f-4fa5-a500-ef21a9984f7d · outbound

This paper cites Jeblick et al.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Jeblick et al

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.799723Z

Source-reported events for the cited work

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Observation 28bd8043-4b7c-4831-9baa-2d695d54959a · outbound

This paper cites A whole-body psma-pet/ct dataset with manu- ally annotated tumor lesions.Scientific Data, 13(1):1023, 2026.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets A whole-body psma-pet/ct dataset with manu- ally annotated tumor lesions.Scientific Data, 13(1):1023, 2026

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.792495Z

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 ae4ed00e-80cd-43f8-8c62-808f7b6e6d64 · outbound

This paper cites an unresolved cited work.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:52:26.784866Z

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Observation 63e7f9c4-6ed8-407f-ae3f-8d74ff1ce8c2 · outbound

This paper cites Deep semisupervised transfer learning for fully automated whole-body tumor quantification and prognosis of cancer on pet/ct.Journal of Nuclear Medicine, 65(4):643–650, 2024.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Deep semisupervised transfer learning for fully automated whole-body tumor quantification and prognosis of cancer on pet/ct.Journal of Nuclear Medicine, 65(4):643–650, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.776889Z

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Observation f7314b8a-dbf1-47d7-8b86-56afc1323aa4 · outbound

This paper cites an unresolved cited work.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:52:26.768424Z

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Observation b4f5b5a2-16aa-45b7-9820-bf7c4bd5354c · outbound

This paper cites Total-body pet/ct: challenges and opportunities.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Total-body pet/ct: challenges and opportunities

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.761164Z

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Observation a593eb74-7e5d-4d5d-9615-2ef06b1d335f · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets DINOv2: Learning Robust Visual Features without Supervision

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T10:52:26.538608Z

Source-reported events for the cited work

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Observation 838f67d6-f013-42da-82b4-c5ad0e497834 · outbound

This paper cites Cross attention transformers for multi-modal unsupervised whole-body pet anomaly detection.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Cross attention transformers for multi-modal unsupervised whole-body pet anomaly detection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.753324Z

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Observation 575e9525-9796-40a0-9cb0-f592fb9a4882 · outbound

This paper cites Outcomes of observation vs stereotactic ablative radiation for oligometastatic prostate cancer: the oriole phase 2 randomized clinical trial.JAMA oncology, 6(5):650–659, 2020.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Outcomes of observation vs stereotactic ablative radiation for oligometastatic prostate cancer: the oriole phase 2 randomized clinical trial.JAMA oncology, 6(5):650–659, 2020

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.745843Z

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Observation dbef42c8-656f-4816-a7ee-33f4a1e26e34 · outbound

This paper cites From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T10:52:26.546917Z

Source-reported events for the cited work

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Observation 99bc0b6c-7d83-4028-aaa5-83dc115c3d4f · outbound

This paper cites Uncertainty quan- tification for deep learning-based metastatic lesion segmentation on whole body pet/ct.Physics in Medicine & Biology, 70(11):115009, 2025.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Uncertainty quan- tification for deep learning-based metastatic lesion segmentation on whole body pet/ct.Physics in Medicine & Biology, 70(11):115009, 2025

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.738966Z

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Observation eb7608b3-1515-4d07-a2c3-32ee849e9ee4 · outbound

This paper cites Segmentation of pet/ct lung cancer lesion images via a semi-supervised improved swinunet model.Biomedical Signal Processing and Control, 113:108797, 2026.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Segmentation of pet/ct lung cancer lesion images via a semi-supervised improved swinunet model.Biomedical Signal Processing and Control, 113:108797, 2026

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.731108Z

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Observation 39c1413d-40eb-488b-b392-7febaefdab6f · outbound

This paper cites an unresolved cited work.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:52:26.723202Z

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Observation a251381f-59e8-4ee3-b52a-d5f618cb38aa · outbound

This paper cites Liver metastases.Nature reviews Disease primers, 7(1):27, 2021.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Liver metastases.Nature reviews Disease primers, 7(1):27, 2021

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.715037Z

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Observation c3746b04-8537-4c3f-806a-f71cc5923ba8 · outbound

This paper cites Active learning with uncertainty-guided sample selection for pet/ct tumor segmentation.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Active learning with uncertainty-guided sample selection for pet/ct tumor segmentation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T10:52:26.560720Z

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Observation a4097c66-94f4-4b96-893e-2e88cebffda1 · outbound

This paper cites Evaluation of the prognostic role of liver metastases on patient outcomes: systematic review and meta-analysis.The Cancer Journal, 29(5):279–284, 2023.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Evaluation of the prognostic role of liver metastases on patient outcomes: systematic review and meta-analysis.The Cancer Journal, 29(5):279–284, 2023

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.702273Z

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

Observation 0a77994e-8ab9-4e11-8062-26fec716f1bf · outbound

This paper cites Automated segmentation of lesions and organs at risk on [68ga] ga-psma-11 pet/ct images using self-supervised learning with swin unetr.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Automated segmentation of lesions and organs at risk on [68ga] ga-psma-11 pet/ct images using self-supervised learning with swin unetr

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.694099Z

Source-reported events for the cited work

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Observation 6c6a476d-bda7-4fda-b024-ac697c94f68a · outbound

This paper cites an unresolved cited work.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:52:26.686205Z

Source-reported events for the cited work

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Observation 38459aac-0d07-4bb9-926f-c704310154bc · outbound

This paper cites Seganypet: Universal promptable segmentation from positron emission tomography images.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Seganypet: Universal promptable segmentation from positron emission tomography images

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:52:26.678314Z

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Observation 3b4d96cf-9f3a-412d-9aeb-272bd622cef7 · outbound

This paper cites an unresolved cited work.

Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets Unresolved cited work

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T10:52:26.518212Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.