Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T16:58:30.455944Z
Paper Citation Record · LEDGER
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets
As of 20 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2508.18612.
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 2026-08-20T06:33:59.587034+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
21 of 21 outbound references displayed
- verified exact1
- verified fuzzy17
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation ae877bd4-0122-49b5-a74f-5594afde6733 · outbound
This paper cites Anatomy guided modality fusion for cancer segmentation in pet ct vol- umes and images.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets Anatomy guided modality fusion for cancer segmentation in pet ct vol- umes and images
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:31.054267Z
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 bdb8c466-bb9d-429c-aa64-99639e0fafd3 · outbound
This paper cites Development and evaluation of two open-source nnu-net models for auto- matic segmentation of lung tumors on pet and ct images with and without respi- ratory motion compensation.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets Development and evaluation of two open-source nnu-net models for auto- matic segmentation of lung tumors on pet and ct images with and without respi- ratory motion compensation
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:31.042791Z
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 e47c6da4-35fc-47c8-99f4-cfcc29834074 · outbound
This paper cites Deep learning tech- niques in pet/ct imaging: A comprehensive review from sinogram to image space.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets Deep learning tech- niques in pet/ct imaging: A comprehensive review from sinogram to image space
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:31.030547Z
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 da8584fe-9800-476c-b674-1f03c665185a · outbound
This paper cites Gatidis and T.
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.979542Z
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 2d3b17d8-1884-4f7b-a81b-ac5d1db65b05 · outbound
This paper cites The autopet challenge: towards fully automated lesion segmentation in oncologic pet/ct imag- ing.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets The autopet challenge: towards fully automated lesion segmentation in oncologic pet/ct imag- ing
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.944367Z
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 eeb3e961-5c9b-4ae8-9d65-78c33436c543 · outbound
This paper cites Results from the autopet challenge on fully automated lesion segmentation in oncologic pet/ct imaging.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets Results from the autopet challenge on fully automated lesion segmentation in oncologic pet/ct imaging
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.934669Z
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 53ccd999-8d69-4339-8fd3-13dbc17c6344 · outbound
This paper cites A whole-body fdg-pet/ct dataset with manually anno- tated tumor lesions.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets A whole-body fdg-pet/ct dataset with manually anno- tated tumor lesions
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.924999Z
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 4762e345-2c95-4306-a939-c7e836b5c2e9 · outbound
This paper cites Automatic segmentation of pet/ct lymphoma using an nnu-net model, 2023.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets Automatic segmentation of pet/ct lymphoma using an nnu-net model, 2023
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.913822Z
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 30772542-a690-4407-9b54-2a29571b44e5 · outbound
This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.902392Z
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 f1f2afe4-36bd-496a-95b3-26540c1ae825 · outbound
This paper cites nnu-net revisited: A call for rigorous vali- dation in 3d medical image segmentation.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets nnu-net revisited: A call for rigorous vali- dation in 3d medical image segmentation
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.808116Z
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 f88bf895-ca71-4401-adb0-5b9ef4cca9b6 · outbound
This paper cites Appropriate use criteria for 18f-fdg pet/ct in restaging and treatment response assessment of malignant disease.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets Appropriate use criteria for 18f-fdg pet/ct in restaging and treatment response assessment of malignant disease
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.762950Z
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 bbfd46ed-9c7c-40b9-8894-fea67438e4e2 · outbound
This paper cites Jeblick et al.
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.751488Z
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 22899bc4-dc1c-4a05-add6-44d3104c34fb · outbound
This paper cites Towards a guideline for evaluation metrics in medical image segmentation.BMC Research Notes, 15(1):210, 2022.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets Towards a guideline for evaluation metrics in medical image segmentation.BMC Research Notes, 15(1):210, 2022
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.740478Z
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 40cdcf8f-8032-40be-b6b1-1d4464571bcb · outbound
This paper cites From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging
Reference 14
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8edebaef-15d2-46a7-9281-be5fea317c75 · outbound
This paper cites U-Net and its variants for medical image segmentation: theory and applications.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets U-Net and its variants for medical image segmentation: theory and applications
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:58:30.308728Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7e9583ca-8f95-46a1-981b-eb8984a63834 · outbound
This paper cites Computational radiomics system to decode the radiographic phenotype.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets Computational radiomics system to decode the radiographic phenotype
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.729386Z
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 92a6a129-0cc1-4108-8abc-98db9832affd · outbound
This paper cites The potential role of AI agents in transforming nuclear medicine research and cancer management in India.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets The potential role of AI agents in transforming nuclear medicine research and cancer management in India
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T16:58:30.552293Z
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 5b65c1da-6191-4697-ad45-5b7f7e121041 · outbound
This paper cites Modular gan: positron emission tomography image reconstruction using two generative adversarial networks.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets Modular gan: positron emission tomography image reconstruction using two generative adversarial networks
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.661154Z
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 14125171-dc96-4261-bca0-af32fd381cb6 · outbound
This paper cites Dual channel CW nnU-Net for 3D PET-CT Lesion Segmentation in 2024 autoPET III Challenge.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets Dual channel CW nnU-Net for 3D PET-CT Lesion Segmentation in 2024 autoPET III Challenge
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:58:30.446320Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c6014fd1-51cf-4098-bb38-e7a64055487d · outbound
This paper cites Robust and generaliz- able artificial intelligence for multi-organ segmentation in ultra-low-dose total-body pet imaging: a multi-center and cross-tracer study.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets Robust and generaliz- able artificial intelligence for multi-organ segmentation in ultra-low-dose total-body pet imaging: a multi-center and cross-tracer study
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.629496Z
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 f8305033-3c33-43e4-8344-da478fd003af · outbound
This paper cites Quantitative analysis of pet studies.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets Quantitative analysis of pet studies
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:58:30.617941Z
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.
Pith citing papers
No inbound Pith citation observations are available.