Paper Citation Record · LEDGER

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation

As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2502.03430.

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

pith.paper-citation-record.v1
2502.03430 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T04:50:00.397106Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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 ce806409-4275-407e-8d5a-82373c8774cf · outbound

This paper cites Berzin, Sravanthi Parasa, Michael B.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Berzin, Sravanthi Parasa, Michael B

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:01.071968Z

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Observation d9b1b1c7-dd80-4e02-8d73-4b52003239c5 · outbound

This paper cites Real-colon: A dataset for developing real-world ai applications in colonoscopy.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Real-colon: A dataset for developing real-world ai applications in colonoscopy

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:01.052934Z

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Observation c742bfa6-de72-497e-8e94-f74d2ed586e7 · outbound

This paper cites A novel AI device for real-time optical characterization of colorectal polyps.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation A novel AI device for real-time optical characterization of colorectal polyps

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:01.031561Z

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Observation f0827063-b6db-4f0d-9663-6031a35977ca · outbound

This paper cites Develop- ment and validation of a deep learning-based algorithm for colonoscopy quality assessment.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Develop- ment and validation of a deep learning-based algorithm for colonoscopy quality assessment

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:01.010631Z

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Observation b65b0566-223b-49fe-ac35-f7f71ec506fa · outbound

This paper cites Tecno: Surgical phase recognition with multi-stage temporal convolutional networks.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Tecno: Surgical phase recognition with multi-stage temporal convolutional networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.994062Z

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Observation a91b8e30-49c1-4e19-bd9c-a74c4e372fb1 · outbound

This paper cites Opera: Attention-regularized transform- ers for surgical phase recognition.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Opera: Attention-regularized transform- ers for surgical phase recognition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.978908Z

Source-reported events for the cited work

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Observation 94f3f74c-1c99-4691-8e68-bbbd3d60bce3 · outbound

This paper cites Automated colonoscopy withdrawal phase duration estimation using ce- cum detection and surgical tasks classification.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Automated colonoscopy withdrawal phase duration estimation using ce- cum detection and surgical tasks classification

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.963538Z

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Observation fce5448c-9b0e-40d3-8692-7362eca29338 · outbound

This paper cites an unresolved cited work.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Unresolved cited work

Reference 8

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Observation 1dae6c48-45be-4da6-a768-61454a9c1e0f · outbound

This paper cites Deep learning in surgical work- flow analysis: A review of phase and step recognition.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Deep learning in surgical work- flow analysis: A review of phase and step recognition

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.928109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 846710fe-5155-46ee-bd5c-b1ea6ab4700c · outbound

This paper cites Temporal action segmen- tation: An analysis of modern techniques.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Temporal action segmen- tation: An analysis of modern techniques

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.910739Z

Source-reported events for the cited work

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Observation 25bc3f6b-e4db-47f3-b865-3f5c14d61d8e · outbound

This paper cites Exploring segment-level semantics for on- line phase recognition from surgical videos.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Exploring segment-level semantics for on- line phase recognition from surgical videos

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.891831Z

Source-reported events for the cited work

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Observation 67233086-4d36-4e64-9e7b-9f061507a895 · outbound

This paper cites Ms-tcn: Multi-stage temporal convolu- tional network for action segmentation.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Ms-tcn: Multi-stage temporal convolu- tional network for action segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.874830Z

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Observation ab3a1fac-e5fd-41af-98f8-2fb3ba5aff12 · outbound

This paper cites Development and valida- tion of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Development and valida- tion of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.855934Z

Source-reported events for the cited work

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Observation ce734833-0f16-4938-87fe-1f068dad459a · outbound

This paper cites Trans-svnet: Accurate phase recognition from surgical videos via hybrid embedding aggregation transformer.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Trans-svnet: Accurate phase recognition from surgical videos via hybrid embedding aggregation transformer

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.839020Z

Source-reported events for the cited work

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Observation e9ba355e-6020-400f-a7e0-cdf46862fb2d · outbound

This paper cites Artificial intelligence applied to colonoscopy: Is it time to take a step forward? Cancers, 15(8):2193, 2023.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Artificial intelligence applied to colonoscopy: Is it time to take a step forward? Cancers, 15(8):2193, 2023

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.821705Z

Source-reported events for the cited work

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Observation d5a7009b-1eb8-47ff-9efa-6caf9139d1f1 · outbound

This paper cites Deep residual learning for image recognition.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Deep residual learning for image recognition

Reference 16

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Source-reported events for the cited work

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Observation c84516cd-da48-494c-af41-0755dd3b89ed · outbound

This paper cites Automatic measurement of quality metrics for colonoscopy videos.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Automatic measurement of quality metrics for colonoscopy videos

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.794573Z

Source-reported events for the cited work

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Observation 77b37117-67e5-41b1-a8fa-268e6c6145cf · outbound

This paper cites Sv-rcnet: workflow recognition from surgical videos using recurrent convolutional network.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Sv-rcnet: workflow recognition from surgical videos using recurrent convolutional network

Reference 18

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Observation 029edef7-84a0-468e-a0c5-cdc025bc66df · outbound

This paper cites Multi-task recurrent convolutional network with corre- lation loss for surgical video analysis.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Multi-task recurrent convolutional network with corre- lation loss for surgical video analysis

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.766478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cace8d43-4256-4bb4-b975-ccc2ea20b145 · outbound

This paper cites Estimating withdrawal time in colonoscopies.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Estimating withdrawal time in colonoscopies

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.748254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 26bb7264-ab7e-4c8f-9fce-8e26f1f774b6 · outbound

This paper cites Semantic parsing of colonoscopy videos with multi-label temporal networks.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Semantic parsing of colonoscopy videos with multi-label temporal networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.730154Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4ed2f652-d0c0-4a9e-aa88-da189a009ff7 · outbound

This paper cites Temporal convolutional networks for action segmentation and de- tection.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Temporal convolutional networks for action segmentation and de- tection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.714077Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 32711648-05d9-4951-9320-d6f7331821ae · outbound

This paper cites Temporal deformable residual networks for action segmentation in videos.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Temporal deformable residual networks for action segmentation in videos

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.695336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 350a0d70-7155-49f3-afe4-48a22a292ef6 · outbound

This paper cites Ms-tcn++: Multi-stage temporal convolutional network for action segmentation.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Ms-tcn++: Multi-stage temporal convolutional network for action segmentation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.678759Z

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Observation c0c74b62-f64a-4ec9-9296-208c6d4f6b47 · outbound

This paper cites Focal loss for dense object detection.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Focal loss for dense object detection

Reference 25

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Source-reported events for the cited work

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Observation 5ea42c0a-0039-4357-9396-16ae38ff4734 · outbound

This paper cites Automated measurement of quality of mucosa inspection for colonoscopy.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Automated measurement of quality of mucosa inspection for colonoscopy

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.650174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0315518c-58fc-4a07-b82f-ac1bda97fdd4 · outbound

This paper cites Multi-task temporal convolutional networks for joint recognition of surgical phases and steps in gastric bypass procedures.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Multi-task temporal convolutional networks for joint recognition of surgical phases and steps in gastric bypass procedures

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.628391Z

Source-reported events for the cited work

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Observation c6922476-e293-4aa8-bf0f-e83f87226ea6 · outbound

This paper cites Key quality indicators in colonoscopy.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Key quality indicators in colonoscopy

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.606420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f55f5ca2-695f-4ef2-a1c7-fd1a3781b080 · outbound

This paper cites Quality indicators in colonoscopy: an evolving paradigm.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Quality indicators in colonoscopy: an evolving paradigm

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.587430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9963c173-0907-471a-8a75-75ca827c77b1 · outbound

This paper cites Patel, Alessandro Fugazza, Gaia Pellegatta, Piera Alessia Galtieri, Gianluca Lollo, Silvia Carrara, Andrea Anderloni, Douglas K.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Patel, Alessandro Fugazza, Gaia Pellegatta, Piera Alessia Galtieri, Gianluca Lollo, Silvia Carrara, Andrea Anderloni, Douglas K

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.564967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5ccc29f8-b12a-4dd2-b10c-c57de69dc776 · outbound

This paper cites Artificial intelligence for colonoscopy: Past, present, and future.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Artificial intelligence for colonoscopy: Past, present, and future

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.541527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f3c55c4a-fe90-4a8b-9ac9-3edf77ee97da · outbound

This paper cites ASFormer: Transformer for Action Segmentation.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation ASFormer: Transformer for Action Segmentation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T04:50:00.379412Z

Source-reported events for the cited work

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Observation 90afd198-336c-483e-aa26-a04b4fab3fd0 · outbound

This paper cites Multi-step validation of a deep learning-based system for the quantification of bowel preparation: a prospective, observational study.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Multi-step validation of a deep learning-based system for the quantification of bowel preparation: a prospective, observational study

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.519753Z

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Observation 0d8d97c5-9acf-4359-bc66-97faf2b30a2f · outbound

This paper cites Deepphase: surgi- cal phase recognition in cataracts videos.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Deepphase: surgi- cal phase recognition in cataracts videos

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.499554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7edabe1e-19c8-44ee-a56d-0cf0d808a7bb · outbound

This paper cites Adenoma detec- tion rate and colorectal cancer risk in fecal immunochemical test screening programs: An observational cohort study.

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation Adenoma detec- tion rate and colorectal cancer risk in fecal immunochemical test screening programs: An observational cohort study

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:50:00.478231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.