Paper Citation Record · LEDGER

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge

As of 9 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2507.04681.

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

pith.paper-citation-record.v1
2507.04681 v3

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:45:19.798495Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:45:17.311812Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:45:20.041968Z

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eec51693-9eec-4372-8a4c-ebb7f3975027 · outbound

This paper cites Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:45:20.127268Z

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Observation 0618d160-61ba-4a01-bc18-6cc3b211fb77 · outbound

This paper cites HardNet+Lawin.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge HardNet+Lawin

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:23.575788Z

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Observation 10fa3129-75f5-46d3-8726-67904d8b4d2c · outbound

This paper cites an unresolved cited work.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:45:23.444339Z

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Observation 8d7c3fc8-855a-4bdc-be9e-beced5c9dcf3 · outbound

This paper cites The challenge attracted broad participation, with 39 teams developing segmentation models for complex multi-class tissue grading and segmentation under limited supervision.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge The challenge attracted broad participation, with 39 teams developing segmentation models for complex multi-class tissue grading and segmentation under limited supervision

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:23.305169Z

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Observation 0e71ec60-8110-4a0b-bb47-38f1a5a3f165 · outbound

This paper cites Global cancer statistics 2020: Globocan estimates of incidence and mortality world- wide for 36 cancers in 185 countries,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Global cancer statistics 2020: Globocan estimates of incidence and mortality world- wide for 36 cancers in 185 countries,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:23.194014Z

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Observation 6ffa99e7-c6d9-4675-8f0a-2f0492877a92 · outbound

This paper cites Colorectal cancer statistics, 2020,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Colorectal cancer statistics, 2020,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:23.048921Z

Source-reported events for the cited work

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Observation 7c8877ee-e36c-4a20-822a-d69c4cb12cbd · outbound

This paper cites Global colorectal cancer bur- den in 2020 and projections to 2040,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Global colorectal cancer bur- den in 2020 and projections to 2040,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:22.925428Z

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Observation 352cdfe7-a6b7-4d83-a675-b1306ddebe19 · outbound

This paper cites The consensus molecular subtypes of colorectal cancer,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge The consensus molecular subtypes of colorectal cancer,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:22.815240Z

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Observation 20c8b4d1-7e28-4554-8b38-af8bfc494c3a · outbound

This paper cites Histological grade predicts survival time asso- ciated with recurrence after resection for colorectal can- cer.,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Histological grade predicts survival time asso- ciated with recurrence after resection for colorectal can- cer.,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:22.702296Z

Source-reported events for the cited work

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Observation 7e971d4e-5cb4-4218-a56e-7d5cb99d756d · outbound

This paper cites Interobserver agree- ment in grading of colorectal cancers—findings from a nationwide web-based survey of histopathologists,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Interobserver agree- ment in grading of colorectal cancers—findings from a nationwide web-based survey of histopathologists,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:22.603348Z

Source-reported events for the cited work

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Observation 2ee48ed5-0088-4326-bba5-61a7d3c7d669 · outbound

This paper cites The eighth edition ajcc cancer staging manual: continuing to build a bridge from a population-based to a more “personalized.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge The eighth edition ajcc cancer staging manual: continuing to build a bridge from a population-based to a more “personalized

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:22.518723Z

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Observation a38f466a-3be3-4752-94f1-6ef728c7af95 · outbound

This paper cites Artificial intelligence for solid tu- mour diagnosis in digital pathology,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Artificial intelligence for solid tu- mour diagnosis in digital pathology,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:22.427962Z

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Observation 84cf494a-90eb-455e-a967-1c391afbc260 · outbound

This paper cites Observer variation in the histolog- ical grading of rectal carcinoma.,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Observer variation in the histolog- ical grading of rectal carcinoma.,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:22.346457Z

Source-reported events for the cited work

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Observation 2490f7cc-38f1-4e01-abb2-100354b07089 · outbound

This paper cites Constant demand, patchy supply,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Constant demand, patchy supply,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:22.232641Z

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Observation dd6c75ce-dd37-4089-9335-0090f652299c · outbound

This paper cites Colorectal cancer tumor grade segmentation: A new dataset and baseline results,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Colorectal cancer tumor grade segmentation: A new dataset and baseline results,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:22.140636Z

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Observation b14b2900-e37f-4431-90a1-aade211cd512 · outbound

This paper cites Unified perceptual parsing for scene un- derstanding,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Unified perceptual parsing for scene un- derstanding,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:22.044755Z

Source-reported events for the cited work

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Observation 519887c8-dd57-45f2-8191-52114e36a40b · outbound

This paper cites Visual attention net- work,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Visual attention net- work,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:21.907123Z

Source-reported events for the cited work

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Observation 8faf9f53-c9fc-47ea-bba2-1396eb844b6c · outbound

This paper cites Fully convolutional networks for semantic segmenta- tion,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Fully convolutional networks for semantic segmenta- tion,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:21.733211Z

Source-reported events for the cited work

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Observation b3474a5d-7804-4729-b56b-605ea0879248 · outbound

This paper cites Towards robust monoc- ular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Towards robust monoc- ular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:21.606731Z

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Observation 297ed60d-9621-402c-a16b-6c4cbd16923a · outbound

This paper cites MaxViT: Multi-axis vision transformer,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge MaxViT: Multi-axis vision transformer,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:21.469409Z

Source-reported events for the cited work

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Observation 5b8eb56d-8d68-443f-afd7-f3b42c234171 · outbound

This paper cites Adaptive Augmentation Policy Optimization with LLM Feedback.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Adaptive Augmentation Policy Optimization with LLM Feedback

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:45:19.980145Z

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Observation d0be7cde-bf4d-47cc-ae1c-5cc6feaddf7d · outbound

This paper cites Sur- vey: interpolation methods for whole slide image pro- cessing,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Sur- vey: interpolation methods for whole slide image pro- cessing,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:21.315559Z

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Observation 141d362a-539e-4847-9769-c7683ec8488a · outbound

This paper cites Hardnet-dfus: Enhancing backbone and decoder of hardnet-mseg for diabetic foot ulcer image segmentation,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Hardnet-dfus: Enhancing backbone and decoder of hardnet-mseg for diabetic foot ulcer image segmentation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:21.168707Z

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Observation 12a379dc-83b6-447e-811e-c7b689ff2d3a · outbound

This paper cites Hardnet: A low memory traffic network,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Hardnet: A low memory traffic network,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:21.006582Z

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Observation 875b96fc-ed49-4a98-b053-4869ea697f20 · outbound

This paper cites Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention

Reference 25

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Observation 35e13a79-5952-4167-9ad9-4c8298e62fb1 · outbound

This paper cites 100,000 histological images of human colorec- tal cancer and healthy tissue,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge 100,000 histological images of human colorec- tal cancer and healthy tissue,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:20.850578Z

Source-reported events for the cited work

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Observation c10b25d1-27c1-4190-b9b5-09c44830d36e · outbound

This paper cites Nct-crc-he: Not all histopathological datasets are equally useful,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Nct-crc-he: Not all histopathological datasets are equally useful,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:20.700247Z

Source-reported events for the cited work

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Observation b27be463-a137-479d-b688-ccf2c457c367 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Overcoming catastrophic forgetting in neural networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:20.587855Z

Source-reported events for the cited work

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Observation fde20c34-a64f-4506-b6e6-435aec9c0bd4 · outbound

This paper cites MMSegmentation: Openmmlab semantic segmentation toolbox and bench- mark,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge MMSegmentation: Openmmlab semantic segmentation toolbox and bench- mark,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:20.440785Z

Source-reported events for the cited work

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Observation c75c3a14-ca5c-4015-b516-4117c68cee5f · outbound

This paper cites Towards a general-purpose foundation model for computational pathology,.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Towards a general-purpose foundation model for computational pathology,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:20.297624Z

Source-reported events for the cited work

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Observation 9167e7f9-2555-46a6-a149-6294af3d4f56 · outbound

This paper cites Vision Transformer Adapter for Dense Predictions.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Vision Transformer Adapter for Dense Predictions

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T19:45:19.798495Z

Source-reported events for the cited work

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Pith citing papers

Observation eec51693-9eec-4372-8a4c-ebb7f3975027 · inbound