Paper Citation Record · LEDGER

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography

As of 22 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2507.17779.

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

pith.paper-citation-record.v1
2507.17779 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:03:50.851419Z

measured 28 of 28 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

28 of 28 outbound references displayed

verified exact0
verified fuzzy27
unresolved1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a9c20d28-7723-4b28-b7c5-bfb45c25fb76 · outbound

This paper cites Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.121597Z

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 382cbae9-8030-442f-b99d-59818562eaef · outbound

This paper cites Image quality in coronary CT angiography: challenges and technical solutions,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Image quality in coronary CT angiography: challenges and technical solutions,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.112710Z

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 3d46db33-a032-4d63-be7c-7514688b2852 · outbound

This paper cites Extraction of coronary arterial tree using cine x-ray angiograms,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Extraction of coronary arterial tree using cine x-ray angiograms,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.103459Z

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 3b16a006-0a9d-4eb1-8dda-e37625fb651c · outbound

This paper cites Self-supervised learning for medical image classification: a systematic review and implementation guidelines,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Self-supervised learning for medical image classification: a systematic review and implementation guidelines,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.093975Z

Source-reported events for the cited work

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Observation 0fa84f2e-15b2-45f2-a1a7-d159ff12d93a · outbound

This paper cites Contrastive Masked Autoencoders are Stronger Vision Learners,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Contrastive Masked Autoencoders are Stronger Vision Learners,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.084681Z

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 efb1a644-e3bb-4275-a283-ae205305f94a · outbound

This paper cites Fractional flow reserve-guided PCI versus medical therapy in stable coronary disease,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Fractional flow reserve-guided PCI versus medical therapy in stable coronary disease,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.075945Z

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 b0f63f50-fe91-45e4-9293-8103e354c3d4 · outbound

This paper cites ImageNet Classifi- cation with Deep Convolutional Neural Networks,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography ImageNet Classifi- cation with Deep Convolutional Neural Networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.067012Z

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 76833724-198e-44e3-a92a-d7cead414982 · outbound

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

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography U-Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.057824Z

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 6e3c61f9-74bd-4936-8988-9ed753bd8401 · outbound

This paper cites Deep residual learning for image recognition,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Deep residual learning for image recognition,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:50.802274Z

Source-reported events for the cited work

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Observation b121d9eb-a1fa-4cc7-a7b5-7bcd0eb6dd40 · outbound

This paper cites Densely connected convolutional networks,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Densely connected convolutional networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.041512Z

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 92f3f56d-c1ef-4bbe-9778-7df75b6abf11 · outbound

This paper cites Deep learning segmentation of major vessels in X-ray coronary angiography,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Deep learning segmentation of major vessels in X-ray coronary angiography,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.032931Z

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 c7136cd3-211f-414f-bbf2-4a98792d4da4 · outbound

This paper cites AngioNet: a convolutional neural network for vessel segmentation in X-ray angiography,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography AngioNet: a convolutional neural network for vessel segmentation in X-ray angiography,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.024410Z

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 ec427581-12a7-4d29-876f-cc599a5ccb9c · outbound

This paper cites AngioPy Segmentation: An open-source, user- guided deep learning tool for coronary artery segmentation,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography AngioPy Segmentation: An open-source, user- guided deep learning tool for coronary artery segmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.015454Z

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 e70eae91-c38b-4cd4-b2c2-e6068f9a181d · outbound

This paper cites Echo-Rhythm Net: Semi-Supervised Learning For Automatic Detection of Atrial Fibrillation in Echocardiography,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Echo-Rhythm Net: Semi-Supervised Learning For Automatic Detection of Atrial Fibrillation in Echocardiography,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:51.006880Z

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 241ebdd3-b344-49f3-8b57-df44bb4e2dce · outbound

This paper cites Self-supervised contrastive video-speech representation learning for ultrasound,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Self-supervised contrastive video-speech representation learning for ultrasound,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.997517Z

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 55793105-e322-4672-b194-5b04ea80ccea · outbound

This paper cites Models Gen- esis: Generic Autodidactic Models for 3D Medical Image Analysis,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Models Gen- esis: Generic Autodidactic Models for 3D Medical Image Analysis,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.988733Z

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 72042bd0-7cbb-4ff8-bc25-6b008a660f62 · outbound

This paper cites A simple frame- work for contrastive learning of visual representations,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography A simple frame- work for contrastive learning of visual representations,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.980701Z

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 7c98d6e0-0474-4e3a-8254-3ad79ac4cfdf · outbound

This paper cites Momentum con- trast for unsupervised visual representation learning,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Momentum con- trast for unsupervised visual representation learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.972348Z

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 ba979c05-12f7-4370-b052-ffc4404df935 · outbound

This paper cites Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.964126Z

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 e450dc2e-6f66-43f6-8e98-364b1bf31738 · outbound

This paper cites Contrastive learning of global and local features for medical image segmentation with limited annotations,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Contrastive learning of global and local features for medical image segmentation with limited annotations,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.954697Z

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 4f1e4bf7-539a-491f-9cb2-a488f6d242da · outbound

This paper cites Masked autoencoders are scalable vision learners,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Masked autoencoders are scalable vision learners,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.945344Z

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 05bd2cde-ba36-4fc4-abb9-5b03440196a7 · outbound

This paper cites Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.936512Z

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 24eb5129-f622-4712-8dd7-c2d1d67a4ca7 · outbound

This paper cites Self-supervised vessel segmentation via adversarial learning,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Self-supervised vessel segmentation via adversarial learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.927326Z

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 c30317ea-db74-406a-8a70-1c6b96345940 · outbound

This paper cites Pretrained subtraction and segmentation model for coronary angiograms,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Pretrained subtraction and segmentation model for coronary angiograms,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.917974Z

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 dafa25ea-f652-41e1-a539-b4027c25781d · outbound

This paper cites Representation Learning with Contrastive Predictive Coding,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Representation Learning with Contrastive Predictive Coding,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.909046Z

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 4bd9eb06-da46-47fd-bf7d-4976869fe14f · outbound

This paper cites cldice-a novel topology-preserving loss function for tubular structure segmentation,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography cldice-a novel topology-preserving loss function for tubular structure segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.899913Z

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 41af6e02-c439-4659-bcf3-cd17aeb16c12 · outbound

This paper cites A modified Hausdorff distance for object matching,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography A modified Hausdorff distance for object matching,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.890923Z

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 29c2da03-0b74-46a8-8c57-edfb4b65b4b0 · outbound

This paper cites Statistical methods for assessing agreement between two methods of clinical measurement,.

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography Statistical methods for assessing agreement between two methods of clinical measurement,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:50.881291Z

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.