Paper Citation Record · LEDGER

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data

As of 11 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2501.11695.

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

pith.paper-citation-record.v1
2501.11695 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:02:48.397307Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f898d5af-b539-4a88-8011-4027e787c46d · outbound

This paper cites Causes and consequences of spatial heterogeneity in ecosystem function.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Causes and consequences of spatial heterogeneity in ecosystem function

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.766133Z

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Observation f290e0ef-acc3-420c-aea3-7e1364aa16ac · outbound

This paper cites Fastmapping: Software to create field maps and identify management zones in precision agriculture.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Fastmapping: Software to create field maps and identify management zones in precision agriculture

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.755727Z

Source-reported events for the cited work

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Observation 31db3860-2976-4ed1-88d9-619e79e41659 · outbound

This paper cites Management of rhesus alloim- munization in pregnancy.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Management of rhesus alloim- munization in pregnancy

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.745385Z

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Observation 60e19bdc-1524-4992-a4ea-fd8c7956c890 · outbound

This paper cites Preparing medical imaging data for machine learning.Radiology, 295(1):4–15, 2020.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Preparing medical imaging data for machine learning.Radiology, 295(1):4–15, 2020

Reference 4

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Observation dccb8eac-a02a-4801-b843-a5d1f7bcff60 · outbound

This paper cites Domain- adversarial training of neural networks.Journal of ma- chine learning research, 17(59):1–35, 2016.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Domain- adversarial training of neural networks.Journal of ma- chine learning research, 17(59):1–35, 2016

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.725746Z

Source-reported events for the cited work

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Observation 4ea8d1cf-ff2c-4745-95ee-fb409873ad0a · outbound

This paper cites Jay Kuo, and Yun Fu.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Jay Kuo, and Yun Fu

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.715995Z

Source-reported events for the cited work

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Observation b3af8504-9e2f-4379-b18e-e59ba71e48ba · outbound

This paper cites Adversarial training on point clouds for sim-to-real 3d object detection.IEEE Robotics and Automation Letters, 6(4):6662–6669, 2021.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Adversarial training on point clouds for sim-to-real 3d object detection.IEEE Robotics and Automation Letters, 6(4):6662–6669, 2021

Reference 7

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Source-reported events for the cited work

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Observation 6cbd76f8-dfbd-4d8d-82cd-0173ef620089 · outbound

This paper cites Maximum classifier discrepancy for unsupervised domain adaptation.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Maximum classifier discrepancy for unsupervised domain adaptation

Reference 8

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Observation dd65101b-1ddc-4406-a8e6-6e315ea7617b · outbound

This paper cites Ad- versarial domain adaptation with domain mixup.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Ad- versarial domain adaptation with domain mixup

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.688268Z

Source-reported events for the cited work

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Observation 8f332256-932f-4742-ba05-63b111147d9e · outbound

This paper cites Self- supervised learning for domain adaptation on point clouds.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Self- supervised learning for domain adaptation on point clouds

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.677733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 40e2b7df-1a5a-4e54-9259-8a2aeec8e9d0 · outbound

This paper cites Domain adaptation on point clouds via geometry-aware implicits.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Domain adaptation on point clouds via geometry-aware implicits

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.667613Z

Source-reported events for the cited work

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Observation 182f372c-30fe-4147-a2c4-475e45cc8bb6 · outbound

This paper cites Geometry-aware self-training for unsupervised domain adaptation on object point clouds.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Geometry-aware self-training for unsupervised domain adaptation on object point clouds

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.657098Z

Source-reported events for the cited work

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Observation c8aa210c-a2da-438d-9afb-86afdec91e7d · outbound

This paper cites A learnable self-supervised task for unsupervised domain adaptation on point cloud classification and segmentation.Frontiers of Computer Science, 17(6):176708, 2023.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data A learnable self-supervised task for unsupervised domain adaptation on point cloud classification and segmentation.Frontiers of Computer Science, 17(6):176708, 2023

Reference 13

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Source-reported events for the cited work

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Observation 22552f2a-912d-42f9-a98b-7090c77b8e7e · outbound

This paper cites Graph convolutional networks: a com- prehensive review.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Graph convolutional networks: a com- prehensive review

Reference 14

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Observation 19fa90b4-cbfa-4379-a859-fde99580a410 · outbound

This paper cites Multiplexed imaging in oncology.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Multiplexed imaging in oncology

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.624910Z

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Observation 112c1978-2de1-4f89-8c45-5d47b84a881d · outbound

This paper cites Cscd: towards spatially resolving the heteroge- neous landscape of mxif oncology data.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Cscd: towards spatially resolving the heteroge- neous landscape of mxif oncology data

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.614288Z

Source-reported events for the cited work

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Observation 53d864b1-e5dc-4004-8f4a-8c65d1075dde · outbound

This paper cites Towards spatially-lucid ai classification in non-euclidean space: An application for mxif oncol- ogy data.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Towards spatially-lucid ai classification in non-euclidean space: An application for mxif oncol- ogy data

Reference 17

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Observation c8f890c9-80df-4d2c-961a-778d53d5e543 · outbound

This paper cites Spatial computing opportunities in biomedical decision support: The atlas-ehr vision.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Spatial computing opportunities in biomedical decision support: The atlas-ehr vision

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.594570Z

Source-reported events for the cited work

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Observation 76d1d3ed-9a32-4ed2-a49f-fa00ac69f851 · outbound

This paper cites Computational pathology: Exploring the spatial dimension of tumor ecology.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Computational pathology: Exploring the spatial dimension of tumor ecology

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.585158Z

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Observation c5e41f67-3a0a-4e40-be6a-8b20fdf77187 · outbound

This paper cites Discovering spatial co-location patterns: A summary of results.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Discovering spatial co-location patterns: A summary of results

Reference 20

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Observation 07f088fe-677b-4b46-9c91-b59c3d0a0c82 · outbound

This paper cites An introduction to spatial data mining.UCGIS., 2020.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data An introduction to spatial data mining.UCGIS., 2020

Reference 21

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Observation 895cc7a8-409e-49a4-8a26-436ba1cf97b8 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data mixup: Beyond Empirical Risk Minimization

Reference 22

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Observation e363aa63-3327-497d-8cbc-e0ef1d88c7de · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Representation Learning with Contrastive Predictive Coding

Reference 23

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Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Unresolved cited work

Reference 24

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Observation 09599943-4cd0-4ba8-bd2e-e8bada6b15e0 · outbound

This paper cites Samcnet: towards a spatially explainable ai approach for classi- fying mxif oncology data.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Samcnet: towards a spatially explainable ai approach for classi- fying mxif oncology data

Reference 25

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Observation eba64b2c-cb79-4c14-8111-549d7be48dbd · outbound

This paper cites Pedregosa, G.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Pedregosa, G

Reference 26

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Source-reported events for the cited work

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Observation e82b9269-4a6e-4f67-b255-40f63c603f87 · outbound

This paper cites Sr- net: A spatial-relationship aware point-set classifica- tion method for multiplexed pathology images.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Sr- net: A spatial-relationship aware point-set classifica- tion method for multiplexed pathology images

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.523317Z

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Observation 1d0eaf78-1818-4ead-be70-2235815f6048 · outbound

This paper cites Generative adversarial networks.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Generative adversarial networks

Reference 28

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Observation 4ac1e75b-b423-45f6-87a5-82794adb2df4 · outbound

This paper cites Conditional adversarial domain adaptation.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Conditional adversarial domain adaptation

Reference 29

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Observation 08be79e3-4e65-45db-a32f-7fdbd2882511 · outbound

This paper cites Generation for unsupervised domain adaptation: A gan-based approach for object classification with 3d point cloud data.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Generation for unsupervised domain adaptation: A gan-based approach for object classification with 3d point cloud data

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.499468Z

Source-reported events for the cited work

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Observation 7e263f24-7821-4ea2-a78e-bcd5f311e440 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Masked autoencoders are scalable vision learners

Reference 31

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Source-reported events for the cited work

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Observation 53ccbdd1-f861-40fa-8834-c6a8ee73b548 · outbound

This paper cites Domain Generalization with MixStyle.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Domain Generalization with MixStyle

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T18:02:48.387318Z

Source-reported events for the cited work

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Observation 8a19412f-9f1b-403f-bb23-3e289283ece1 · outbound

This paper cites Point-bert: Pre-training 3d point cloud transformers with masked point model- ing.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Point-bert: Pre-training 3d point cloud transformers with masked point model- ing

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.482253Z

Source-reported events for the cited work

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Observation 9035c325-9d1b-489d-9997-d7c7b81ab62b · outbound

This paper cites Self-ensembling for visual domain adaptation.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Self-ensembling for visual domain adaptation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.470616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 44043dde-5c9d-473b-b7de-d9890d48b3d7 · outbound

This paper cites Bidi- rectional learning for domain adaptation of semantic segmentation.

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data Bidi- rectional learning for domain adaptation of semantic segmentation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:02:48.459084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.