Paper Citation Record · LEDGER

The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:2307.01984.

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

pith.paper-citation-record.v1
2307.01984 v1

Coverage vector

measured 0 of 0 reference resolution

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measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:48:18.946538Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T13:16:58.477404Z

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External citation measurements

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Outbound references

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Pith citing papers

Observation 56096954-c4af-4e98-888e-9ecbf438d173 · inbound

Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation cites this paper.

Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-24T01:43:42.892095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7e7bbfd6-056e-4e79-afe1-ab52e4febcd2 · inbound

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation cites this paper.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T13:56:44.855200Z

Source-reported events for the cited work

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Observation 4fce5b9a-1ba3-40d7-997e-773049000163 · inbound

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

Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T19:47:52.568718Z

Source-reported events for the cited work

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Observation 5bf9aaa1-04b9-4020-b262-b35d3717f005 · inbound

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models cites this paper.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T00:48:18.946538Z

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Observation 3e2b9710-4887-43c4-a1ea-e969d780f0ec · inbound

Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration cites this paper.

Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:45.827481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5854b9f9-3d5c-46ce-bdd3-5a798b5d0517 · inbound

Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework cites this paper.

Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:26.724018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab379d83-9679-4011-a576-0f3525996ab4 · inbound

HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation cites this paper.

HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:35.542587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78e72979-b483-4b69-a7d7-8ee28a7d0b93 · inbound

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer cites this paper.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T14:12:18.238250Z

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Unavailable: canonical work link unavailable.

Observation 4b04bb4b-0b8c-42ac-b63e-0b50daeb49a9 · inbound

Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI cites this paper.

Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T10:31:14.824085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f4551c03-721e-48e2-946d-43557d6119a7 · inbound

Submanifold Sparse Convolutional Networks for Automated 3D Segmentation of Kidneys and Kidney Tumours in Computed Tomography cites this paper.

Submanifold Sparse Convolutional Networks for Automated 3D Segmentation of Kidneys and Kidney Tumours in Computed Tomography The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:02:15.264677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ced231b1-d5dc-4c8f-8969-06e172cbe3f0 · inbound

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images cites this paper.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T22:08:33.820352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5df8b75e-9667-4484-9dd1-1d9708a0315e · inbound

Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation cites this paper.

Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T19:04:21.347062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9c269c9-90ea-48a4-9b8d-179519341eec · inbound

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models cites this paper.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:00.245192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f22a239-5d6c-446a-8e54-d2fa12c6c1be · inbound

Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment cites this paper.

Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:41:25.234496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1d642a22-234b-491d-8ef9-e14a4d6fb84d · inbound

GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks cites this paper.

GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:36:26.567725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 39349950-9fd5-4930-8442-a458e179d663 · inbound

SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation cites this paper.

SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:12:28.068981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1ca94382-c5a4-4130-a3da-30acc052f825 · inbound

TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT cites this paper.

TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T18:58:53.958782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 728ebcd6-4e09-45b3-a697-5b9dceb035d9 · inbound

Benchmarking transferability of SSL pretraining to same and different modality segmentation tasks cites this paper.

Benchmarking transferability of SSL pretraining to same and different modality segmentation tasks The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-20T11:33:14.543868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6549f50b-df65-48af-883c-70eba8c52d31 · inbound

MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models cites this paper.

MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:16:58.479093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 94f498a4-deb1-40bf-8e1e-25cff83fe1e6 · inbound

Foundation-model-guided radiogenomic discovery linking cancer genomes to cancer scans cites this paper.

Foundation-model-guided radiogenomic discovery linking cancer genomes to cancer scans The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T10:55:46.074796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22e83790-4987-4d53-8cf2-2d0065f8d83c · inbound

BATS: Resource-Efficient Volumetric Segmentation with Boundary-Aware Mixed-Resolution Tokens cites this paper.

BATS: Resource-Efficient Volumetric Segmentation with Boundary-Aware Mixed-Resolution Tokens The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-30T19:52:25.000450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.