Paper Citation Record · LEDGER

The Power of Certainty: How Confident Models Lead to Better Segmentation

As of 22 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.10490.

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

pith.paper-citation-record.v1
2507.10490 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:35:49.327904Z

measured 36 of 36 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 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

36 of 36 outbound references displayed

verified exact0
verified fuzzy28
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 17b48eff-7573-4dcd-978b-a9d11a59504f · outbound

This paper cites who.int/news-room/fact-sheets/detail/colorectal-cancer, 2023.

The Power of Certainty: How Confident Models Lead to Better Segmentation who.int/news-room/fact-sheets/detail/colorectal-cancer, 2023

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.729033Z

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 3beca521-dfaa-47e4-aa95-67f4f50bdbf3 · outbound

This paper cites Kvasir- seg: A segmented polyp dataset.

The Power of Certainty: How Confident Models Lead to Better Segmentation Kvasir- seg: A segmented polyp dataset

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.719101Z

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 06fdb7cd-3bb5-4d4d-8b0e-f069ecc6dfd2 · outbound

This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without con- volutions.

The Power of Certainty: How Confident Models Lead to Better Segmentation Pyramid vision transformer: A versatile backbone for dense prediction without con- volutions

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T17:35:49.201416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4e6490a-4909-4117-8552-c6f4e72e9244 · outbound

This paper cites A multi-centre polyp detection and segmentation dataset for generalisability assessment.

The Power of Certainty: How Confident Models Lead to Better Segmentation A multi-centre polyp detection and segmentation dataset for generalisability assessment

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.703223Z

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 cb9b1570-9bff-41d8-8a0f-76e52f009fcc · outbound

This paper cites Transnetr: Transformer- basedresidualnetworkforpolypsegmentationwithmulti-centerout- of-distribution testing.

The Power of Certainty: How Confident Models Lead to Better Segmentation Transnetr: Transformer- basedresidualnetworkforpolypsegmentationwithmulti-centerout- of-distribution testing

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.693754Z

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 98d27d91-c33a-4e08-b894-5f80477c68b9 · outbound

This paper cites Shallow attention network for polyp segmentation.

The Power of Certainty: How Confident Models Lead to Better Segmentation Shallow attention network for polyp segmentation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.684102Z

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 7a880a6b-ad87-4964-b5ed-7c3ab6c4cd38 · outbound

This paper cites Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs.

The Power of Certainty: How Confident Models Lead to Better Segmentation Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.674095Z

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 9a222662-48d2-4f9d-bf08-a3e2ada64163 · outbound

This paper cites A benchmark for endoluminal scene segmen- tation of colonoscopy images.

The Power of Certainty: How Confident Models Lead to Better Segmentation A benchmark for endoluminal scene segmen- tation of colonoscopy images

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.663528Z

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 6f27b662-ab04-47fc-8be9-c3589ce51a25 · outbound

This paper cites Toward embedded detection of polyps in wce imagesforearlydiagnosisofcolorectalcancer.

The Power of Certainty: How Confident Models Lead to Better Segmentation Toward embedded detection of polyps in wce imagesforearlydiagnosisofcolorectalcancer

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.653311Z

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 cbe53f8c-2e9c-49b2-b934-ea4a1f1d64a3 · outbound

This paper cites Neounet: Towards accurate colon polyp segmentation and neoplasm detection.

The Power of Certainty: How Confident Models Lead to Better Segmentation Neounet: Towards accurate colon polyp segmentation and neoplasm detection

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.643678Z

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 a29a7de2-dd33-4092-94a7-45c1f7b5979a · outbound

This paper cites Auto- matedpolypdetectionincolonoscopyvideosusingshapeandcontext information.

The Power of Certainty: How Confident Models Lead to Better Segmentation Auto- matedpolypdetectionincolonoscopyvideosusingshapeandcontext information

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.633148Z

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 18e6f4a5-bfd2-44cf-aa0b-d9b448e1cbc7 · outbound

This paper cites U-net: Con- volutional networks for biomedical image segmentation.

The Power of Certainty: How Confident Models Lead to Better Segmentation U-net: Con- volutional networks for biomedical image segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.622870Z

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 cd7137c8-9129-4b2d-ae97-047a99d9b02c · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

The Power of Certainty: How Confident Models Lead to Better Segmentation Attention U-Net: Learning Where to Look for the Pancreas

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T17:35:49.240337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ffa3f5e-c7a7-410d-b7a8-f22a720eba16 · outbound

This paper cites Unet++:Anestedu-netarchitectureformedical image segmentation.

The Power of Certainty: How Confident Models Lead to Better Segmentation Unet++:Anestedu-netarchitectureformedical image segmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.612933Z

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 3da9ea9e-d8b2-413e-b171-6c4ffe46d02e · outbound

This paper cites Unet3+:Afull-scaleconnectedunetformedicalimagesegmentation.

The Power of Certainty: How Confident Models Lead to Better Segmentation Unet3+:Afull-scaleconnectedunetformedicalimagesegmentation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.602253Z

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 ed6e488d-c193-47d6-89ed-04a68e7f0650 · outbound

This paper cites Segnet:A deepconvolutionalencoder-decoderarchitectureforimagesegmenta- tion.

The Power of Certainty: How Confident Models Lead to Better Segmentation Segnet:A deepconvolutionalencoder-decoderarchitectureforimagesegmenta- tion

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.591535Z

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 42caa970-0a80-4aca-921e-391ac81cfc76 · outbound

This paper cites Fully convo- lutional networks for semantic segmentation.

The Power of Certainty: How Confident Models Lead to Better Segmentation Fully convo- lutional networks for semantic segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.580263Z

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 906e103b-c49d-4bdc-8dfe-ef3191b0ebe2 · outbound

This paper cites Pyramidsceneparsingnetwork.

The Power of Certainty: How Confident Models Lead to Better Segmentation Pyramidsceneparsingnetwork

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.569020Z

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 6e225ea6-8e35-414d-aa1e-8540810386f1 · outbound

This paper cites Encoder-decoder with atrous separable con- volution for semantic image segmentation.

The Power of Certainty: How Confident Models Lead to Better Segmentation Encoder-decoder with atrous separable con- volution for semantic image segmentation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.558533Z

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 a1be8ff3-a061-4852-b278-fc375b10dd1e · outbound

This paper cites Road extraction bydeepresidualu-net.

The Power of Certainty: How Confident Models Lead to Better Segmentation Road extraction bydeepresidualu-net

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.546221Z

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 8e8d175a-e3e9-4d71-9c93-1f08d8e6db6c · outbound

This paper cites Cascaded partial decoder for fast and accurate salient object detection.

The Power of Certainty: How Confident Models Lead to Better Segmentation Cascaded partial decoder for fast and accurate salient object detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.535688Z

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 d3f6b77a-90fb-4c63-8c95-9c85da395dfc · outbound

This paper cites Pranet: Parallel reverse attention network for polyp segmentation.

The Power of Certainty: How Confident Models Lead to Better Segmentation Pranet: Parallel reverse attention network for polyp segmentation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T17:35:49.275840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc032ba3-7637-400e-b133-ab115878a91e · outbound

This paper cites Automatic polyp segmentation via multi-scale subtraction network.

The Power of Certainty: How Confident Models Lead to Better Segmentation Automatic polyp segmentation via multi-scale subtraction network

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.518705Z

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 0b9c8104-26fc-49b6-972e-35761d7df7f6 · outbound

This paper cites HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS.

The Power of Certainty: How Confident Models Lead to Better Segmentation HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T17:35:49.283338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 289dfe8b-69bc-44c4-8ed1-165c8b042ce8 · outbound

This paper cites Hardnet: A low memory traffic network.

The Power of Certainty: How Confident Models Lead to Better Segmentation Hardnet: A low memory traffic network

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.506848Z

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 518cd375-4e24-44f9-9598-6f88e3cc4556 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

The Power of Certainty: How Confident Models Lead to Better Segmentation Distilling the Knowledge in a Neural Network

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T17:35:49.290173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4624f71-5e8a-4c37-b02f-2031292cdbab · outbound

This paper cites FitNets: Hints for Thin Deep Nets.

The Power of Certainty: How Confident Models Lead to Better Segmentation FitNets: Hints for Thin Deep Nets

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T17:35:49.294241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b3e306a-266b-417b-8d43-539c2d8b6797 · outbound

This paper cites Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer.

The Power of Certainty: How Confident Models Lead to Better Segmentation Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T17:35:49.297930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8317032-8c86-4a0a-99d7-dd3b6e55ecfc · outbound

This paper cites Relational knowledge distillation.

The Power of Certainty: How Confident Models Lead to Better Segmentation Relational knowledge distillation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.494231Z

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 36bf4eea-168c-441a-86b3-298d4994bd1a · outbound

This paper cites Beyourownteacher:Improvetheperformanceof convolutional neural networks via self distillation.

The Power of Certainty: How Confident Models Lead to Better Segmentation Beyourownteacher:Improvetheperformanceof convolutional neural networks via self distillation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.482534Z

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 116b4666-2993-4d3c-9406-2b6f33c33c13 · outbound

This paper cites Born again neural networks.

The Power of Certainty: How Confident Models Lead to Better Segmentation Born again neural networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T17:35:49.309987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe1bed73-e0c8-4bb6-afbb-27f09e6280f3 · outbound

This paper cites Self-distillation from the last mini-batch for consistency regulariza- tion.

The Power of Certainty: How Confident Models Lead to Better Segmentation Self-distillation from the last mini-batch for consistency regulariza- tion

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.461519Z

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 1b522914-8d9c-4712-9c9f-0afd131ded73 · outbound

This paper cites Receptive field block net for accurate andfastobjectdetection.

The Power of Certainty: How Confident Models Lead to Better Segmentation Receptive field block net for accurate andfastobjectdetection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.448798Z

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 3315d675-b914-4f63-af3a-555709358653 · outbound

This paper cites Deep layer aggregation.

The Power of Certainty: How Confident Models Lead to Better Segmentation Deep layer aggregation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.437404Z

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 36d628c7-e492-4ef7-bf3b-9dc64e8d19c7 · outbound

This paper cites Decoupled weight decay regular- ization, 2019.

The Power of Certainty: How Confident Models Lead to Better Segmentation Decoupled weight decay regular- ization, 2019

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.425134Z

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 d4bc9886-5654-4b3f-94d0-13129e8df014 · outbound

This paper cites Deep residual learning for image recognition.

The Power of Certainty: How Confident Models Lead to Better Segmentation Deep residual learning for image recognition

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:35:49.412656Z

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.

Pith citing papers

No inbound Pith citation observations are available.