Paper Citation Record · LEDGER

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images

As of 18 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2501.12323.

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

pith.paper-citation-record.v1
2501.12323 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:19:59.895260Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:19:59.797896Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T17:19:59.996040Z

Reference resolution

25 of 25 outbound references displayed

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External citation measurements

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Outbound references

Observation 1fc2409f-278e-4836-9c9c-abae6de32d0a · outbound

This paper cites One of the hallmarks of cancer is inducing angio- genesis, a process through which tumors stimulate the for- mation of new blood vessels to sustain their growth.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images One of the hallmarks of cancer is inducing angio- genesis, a process through which tumors stimulate the for- mation of new blood vessels to sustain their growth

Reference 1

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Observation efbbf299-8b2c-426c-b932-29752ff1b6b3 · outbound

This paper cites Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:20:00.002136Z

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Observation 3c609cc4-3c79-496c-bea0-afe46e6bed5a · outbound

This paper cites The HSV color space represents Hue, Saturation, and Value, while the LAB color space represents Lightness (L), and the op- ponent color channels A (green-red) and B (blue-yellow).

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images The HSV color space represents Hue, Saturation, and Value, while the LAB color space represents Lightness (L), and the op- ponent color channels A (green-red) and B (blue-yellow)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:20:00.299170Z

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Observation 55e7ed4e-eecb-4c89-bda9-4f28234bd1ea · outbound

This paper cites an unresolved cited work.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:20:00.284757Z

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Observation 23c14281-feec-4a14-b1c7-218c1d041031 · outbound

This paper cites an unresolved cited work.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Unresolved cited work

Reference 5

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Observation 40b0a737-ad56-4fb6-9d4c-7259c12baa3b · outbound

This paper cites The aim is to remove small objects and small holes.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images The aim is to remove small objects and small holes

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:20:00.254075Z

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Observation 82c31115-15f7-41a6-86f8-69dfddd667c7 · outbound

This paper cites an unresolved cited work.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Unresolved cited work

Reference 7

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Observation d8ec8bc8-1eeb-411b-9182-ebe9b80c5a37 · outbound

This paper cites an unresolved cited work.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Unresolved cited work

Reference 8

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Source-reported events for the cited work

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Observation 222997f5-787e-4915-a3bb-bfa22c3cc469 · outbound

This paper cites Dataset and metrics The dataset for this study was obtained from Imperial Col- lege London where H&E stained WSIs of oesophageal tis- sue were collected.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Dataset and metrics The dataset for this study was obtained from Imperial Col- lege London where H&E stained WSIs of oesophageal tis- sue were collected

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:20:00.207594Z

Source-reported events for the cited work

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Observation 3df582fc-12ac-4a7c-9d0e-cd152a791560 · outbound

This paper cites an unresolved cited work.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Unresolved cited work

Reference 10

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Observation a97e2635-4b9c-41d4-b9a3-f5d40d71804d · outbound

This paper cites an unresolved cited work.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Unresolved cited work

Reference 11

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Observation 3adca5e7-10c1-45f6-8fc0-b34a6120df35 · outbound

This paper cites an unresolved cited work.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Unresolved cited work

Reference 12

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Observation 2c1fcdff-dd5b-4416-bf2c-4d3b3f2ff423 · outbound

This paper cites an unresolved cited work.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Unresolved cited work

Reference 13

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Observation 8705ff1d-4598-4eaf-928f-a9953fd2cb1a · outbound

This paper cites JL is supported by the UK Engineering and Physical Sciences Research Council (EPSRC).

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images JL is supported by the UK Engineering and Physical Sciences Research Council (EPSRC)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:20:00.136428Z

Source-reported events for the cited work

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Observation 783a231f-4436-478d-a44e-476e9fbc5bac · outbound

This paper cites Hallmarks of cancer: the next generation,.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Hallmarks of cancer: the next generation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:20:00.120226Z

Source-reported events for the cited work

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Observation 3d006bea-2136-4954-b01d-4e93d28d51c4 · outbound

This paper cites Vessel de- lineation using u-net: A sparse labeled deep learning approach for semantic segmentation of histological im- ages,.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Vessel de- lineation using u-net: A sparse labeled deep learning approach for semantic segmentation of histological im- ages,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:20:00.106068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c43596ff-0ded-47cc-a57f-5c7246552975 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 17

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Source-reported events for the cited work

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Observation 2807c9b1-1473-4e24-ab2e-aaca2dfcef34 · outbound

This paper cites Fabnet : fea- ture attention-based network for simultaneous segmen- tation of microvessels and nerves in routine histology images of oral cancer,.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Fabnet : fea- ture attention-based network for simultaneous segmen- tation of microvessels and nerves in routine histology images of oral cancer,

Reference 18

Resolution: verified exact; doi, observed 2026-08-10T17:20:00.091046Z

Source-reported events for the cited work

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Observation c4f5f54e-2001-492d-95f9-838650c7c385 · outbound

This paper cites Microvessel pre- diction in h&e stained pathology images using fully con- volutional neural networks,.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Microvessel pre- diction in h&e stained pathology images using fully con- volutional neural networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:20:00.075725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 44264916-a919-43fd-b317-2292ae2ec685 · outbound

This paper cites Au- tomated quantification of blood microvessels in hema- toxylin and eosin whole slide images,.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Au- tomated quantification of blood microvessels in hema- toxylin and eosin whole slide images,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:20:00.059502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 39548c92-e2bb-4c43-bbb3-a06140d0ac54 · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T17:19:59.879130Z

Source-reported events for the cited work

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Observation 3de22eb3-24a4-4eff-8d68-e4fe8e7e823f · outbound

This paper cites EfficientNetV2: Smaller Models and Faster Training.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images EfficientNetV2: Smaller Models and Faster Training

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T17:19:59.883125Z

Source-reported events for the cited work

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Observation 9fa23412-ff64-4435-985d-46ee009df737 · outbound

This paper cites Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:19:59.946842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4c7bfa83-66b7-484a-aa53-ac5eb014763e · outbound

This paper cites Segmentation models py- torch,.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images Segmentation models py- torch,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:20:00.031504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ac2424e1-8be6-4ab5-be25-85af36647081 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T17:19:59.895260Z

Source-reported events for the cited work

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Pith citing papers

Observation efbbf299-8b2c-426c-b932-29752ff1b6b3 · inbound