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Paper Citation Record · LEDGER

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:1904.00445.

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

pith.paper-citation-record.v1
1904.00445 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:44:31.587458Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:49:58.512052Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation afc0414d-964c-4964-a046-9c61803b3df4 · inbound

MozzaVID: Mozzarella Volumetric Image Dataset cites this paper.

MozzaVID: Mozzarella Volumetric Image Dataset The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 36

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verified exact
arxiv_id, observed 2026-05-23T08:02:43.211475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 9d775aaf-7700-4089-a197-0728d964810d · inbound

Primus: Enforcing Attention Usage for 3D Medical Image Segmentation cites this paper.

Primus: Enforcing Attention Usage for 3D Medical Image Segmentation The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-23T01:25:16.543129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 5759eef4-8e4f-4263-b529-66023c978293 · inbound

Comparing the Effects of Persistence Barcodes Aggregation and Feature Concatenation on Medical Imaging cites this paper.

Comparing the Effects of Persistence Barcodes Aggregation and Feature Concatenation on Medical Imaging The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 29

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no resolver link, observed 2026-08-07T12:44:31.587458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:44:31.587458Z digest=sha256:2593bc704184dce583613f25c8c6a9321dc99f9c09efd23a51b56073e4b1f3e3

Observation c954b34e-2073-4c57-8471-b7a2cca1d035 · inbound

Reasoning in machine vision by learning fast and slow thinking cites this paper.

Reasoning in machine vision by learning fast and slow thinking The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 65

Resolution
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no resolver link, observed 2026-08-06T22:16:21.165818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:16:21.165818Z digest=sha256:d6fcf6664be61397fa3807e48be6e7ae2f704bd8fcb74aeaf4bfeb5d10793d80

Observation 11592afc-7801-4647-ae3b-596743df70ce · inbound

PanTS: The Pancreatic Tumor Segmentation Dataset cites this paper.

PanTS: The Pancreatic Tumor Segmentation Dataset The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T21:02:29.329696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:02:29.329696Z digest=sha256:a76ddae64e7f2b5314cfa913106f8953a6aa9b547af428f08c915cf8b965a5a5

Observation 2c618fd0-7eb1-40a7-a141-ad40f79f972e · inbound

Learning Segmentation from Radiology Reports cites this paper.

Learning Segmentation from Radiology Reports The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 13

Resolution
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no resolver link, observed 2026-08-06T19:30:54.057497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:30:54.057497Z digest=sha256:9edc7a45da6fff0292a2d23ae1385a72dea36e1f5f883a7dd77e386b3e41ddc6

Observation 184274c1-6b44-4025-8a17-02704e7cdce6 · inbound

Adaptively Distilled ControlNet: Accelerated Training and Superior Sampling for Medical Image Synthesis cites this paper.

Adaptively Distilled ControlNet: Accelerated Training and Superior Sampling for Medical Image Synthesis The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T10:36:39.478197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:36:39.478197Z digest=sha256:c08e4f1c117ba8f3981942f13fc5bb176b9811005a84a6bc530fff3c4275c929

Observation b7294a89-2e94-4f0c-a9de-111d40d6be65 · inbound

MaLV-OS: Rethinking the Operating System Architecture for Machine Learning in Virtualized Clouds cites this paper.

MaLV-OS: Rethinking the Operating System Architecture for Machine Learning in Virtualized Clouds The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T04:17:30.165981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 590393f4-6527-4477-af9a-8fd3c791a38a · inbound

SED Fitting of Globular Clusters in NGC 4874: Masses and Metallicities cites this paper.

SED Fitting of Globular Clusters in NGC 4874: Masses and Metallicities The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T04:15:06.216015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:15:06.216015Z digest=sha256:e8fd54298ac17396a514fd969350c69f72eb3b11922fe66325cb690ab4efe701

Observation 7ce2e45c-6ec6-4098-8c0e-bc0e8a999192 · inbound

A Novel Patch-Based TDA Approach for Computed Tomography Imaging cites this paper.

A Novel Patch-Based TDA Approach for Computed Tomography Imaging The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-16T23:28:40.406952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c9c266d9-f136-43d1-a0ee-42d9998a6da1 · inbound

RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation cites this paper.

RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-11T08:40:58.315418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 44a35dc9-ba21-4d15-83a7-7352f53468ff · inbound

DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark cites this paper.

DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 25

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T20:31:12.453269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T08:44:22.104750Z digest=sha256:3f41cd237c8d5a430bd81abc365689d027348f729d16734c61b042b4dedb9504

Observation 75a93494-3444-4c02-bc69-541ee22ea728 · inbound

Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection cites this paper.

Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:01:09.233289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-09T14:13:56.762945Z digest=sha256:97751cacebc991eec83af2d05429f0098918fbd013403b754271cd1bf8ef48b8

Observation 2c1e54e4-37b6-4a14-91d0-1247e930aca0 · inbound

RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology cites this paper.

RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-12T03:56:21.517949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-12T03:55:55.359488Z digest=sha256:f13a51cd38bbe9ee5cde0077c7f8a6fefb49cfb9d0efe84a563d60a7c354e80c

Observation 0ec840b1-4841-4560-9b5e-a56a03382c5b · inbound

BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases cites this paper.

BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 21

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T16:49:58.513290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-26T00:08:20.172801Z digest=sha256:9c791d82b627f25261c821c63a7ad8fcda0379570584aaf0a5e84950da798b41

Observation a6183963-8e3a-4ec2-9030-93586f27178b · inbound

Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution cites this paper.

Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 10

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T14:39:57.928047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-26T03:06:30.684065Z digest=sha256:1ff7a44e85f1317bec73996928deabbd65dab1c654ed396458458c3841c5ac9f

Observation e8affee6-62ce-4438-8677-dddc59accea0 · inbound

Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution cites this paper.

Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 10

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T15:13:32.294711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 63e1e043-cb0c-4e44-afcb-7aaad0afaf76 · inbound

Beyond scalar losses: calibrating segmentation models via gradient vector field surgery cites this paper.

Beyond scalar losses: calibrating segmentation models via gradient vector field surgery The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-02T02:30:59.956238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation be2c8871-13bb-4197-9409-8595fc17c7e8 · inbound

Test-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guided cites this paper.

Test-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guided The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T00:53:22.302703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T00:53:22.302703Z digest=sha256:fca0f89de906dec53cce0e651a2648f2e2cf49cfdf1cd84d3aeab0ee442401a1