Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:45:19.798495Z
Paper Citation Record · LEDGER
Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge
As of 16 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:45:17.311812Z
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
- verified exact2
- verified fuzzy26
- unresolved3
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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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Unavailable: canonical work link unavailable.
Pith citing papers
Observation eec51693-9eec-4372-8a4c-ebb7f3975027 · inbound
Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge cites this paper.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.