Paper Citation Record · LEDGER

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation

As of 21 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2509.02602.

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

pith.paper-citation-record.v1
2509.02602 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:52:32.578027Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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 exact5
verified fuzzy6
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 29270e04-f44a-49fc-ad94-58926a05ce5a · outbound

This paper cites ‘Automated Lesion Segmentation in Whole-body PET/CT and Longitudinal (autopet/ct IV)’.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation ‘Automated Lesion Segmentation in Whole-body PET/CT and Longitudinal (autopet/ct IV)’

Reference 1

Resolution: verified exact; doi, observed 2026-08-05T13:52:32.756141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7694bb65-feef-49ce-82e7-7b954db2a361 · outbound

This paper cites Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:52:34.697113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a910c846-a7e0-4e44-a345-878b98683b57 · outbound

This paper cites an unresolved cited work.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:52:34.566064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b0cefe1e-2fe0-43b9-8aef-2f92cdd4ff65 · outbound

This paper cites Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge

Reference 4

Resolution: verified exact; raw_fallback, observed 2026-08-05T13:52:33.574873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 04661d00-b6e3-4003-b741-db53145b438e · outbound

This paper cites Benchmarking the cow with the topcow challenge: Topology-aware anatomical segmentation of the circle of willis for cta and mra.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation Benchmarking the cow with the topcow challenge: Topology-aware anatomical segmentation of the circle of willis for cta and mra

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:52:34.399444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 52ba06a4-cc8b-4752-8632-beccc573a5f8 · outbound

This paper cites ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:52:33.382942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5745726e-6ca4-4bb1-9acd-85f2847cbbce · outbound

This paper cites Multi -center fetal brain tissue annota- tion (feta) challenge 2022 results.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation Multi -center fetal brain tissue annota- tion (feta) challenge 2022 results

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:52:34.235718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c68f1316-eb22-4586-b9ca-7553a6d7bd59 · outbound

This paper cites Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T13:52:31.863555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd0d3ddf-16d5-4838-8720-82f4063212e1 · outbound

This paper cites Transforming Heart Chamber Imaging: Self-Supervised Learning for Whole Heart Reconstruction and Segmentation.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation Transforming Heart Chamber Imaging: Self-Supervised Learning for Whole Heart Reconstruction and Segmentation

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:52:33.148783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0f3490a6-12e9-4650-92b1-83853a125eaf · outbound

This paper cites Hunting imaging biomarkers in pulmonary fibrosis: benchmarks of the AIIB23 challenge.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation Hunting imaging biomarkers in pulmonary fibrosis: benchmarks of the AIIB23 challenge

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:52:34.075459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d82e8a05-2b35-4239-9225-3c0b01c879cc · outbound

This paper cites A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:52:32.980176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 32d6f2b4-ae5c-4297-9228-95c56e0f0fe8 · outbound

This paper cites AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T13:52:32.398020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a199096a-ed27-4295-af7c-95946bc1d15f · outbound

This paper cites nnU-Net: a self-configuring method for deep learning-based biomed- ical image segmentation.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation nnU-Net: a self-configuring method for deep learning-based biomed- ical image segmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:52:33.890825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 75863f08-b934-4da8-bb02-2cec805b1e4e · outbound

This paper cites xlstm: Extended long short-term memory.

Masked Autoencoder Pretraining and BiXLSTM ResNet Architecture for PET/CT Tumor Segmentation xlstm: Extended long short-term memory

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:52:33.760560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.