Paper Citation Record · LEDGER

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation

As of 21 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2412.00715.

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

pith.paper-citation-record.v1
2412.00715 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:08:42.158523Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

26 of 26 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 25fa8446-be0c-40e8-bde6-3b759b218dae · outbound

This paper cites Semi- supervised learning for network-based cardiac mr image segmentation,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Semi- supervised learning for network-based cardiac mr image segmentation,

Reference 1

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Observation 775722a2-35c8-4d23-83d5-87e1679af742 · outbound

This paper cites Bidirectional copy-paste for semi-supervised medical image segmentation,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Bidirectional copy-paste for semi-supervised medical image segmentation,

Reference 2

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Observation fbfaae86-09b7-43fa-806d-8fe527c22a25 · outbound

This paper cites Decoupled consistency for semi-supervised medical image segmentation,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Decoupled consistency for semi-supervised medical image segmentation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.550803Z

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Observation fe053cf1-2119-45c8-8104-98c1ffe77159 · outbound

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

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 4

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Observation 8c7f53e5-e716-43f0-864d-6919a6d3310c · outbound

This paper cites Complexmix: Semi- supervised semantic segmentation via mask-based data augmentation,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Complexmix: Semi- supervised semantic segmentation via mask-based data augmentation,

Reference 5

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Observation 6e15ab1b-ef7f-476a-9fb3-f9eb2d5da094 · outbound

This paper cites Semi-supervised semantic segmentation needs strong, varied perturbations.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Semi-supervised semantic segmentation needs strong, varied perturbations

Reference 6

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Observation dadccc9f-a3da-4a05-923e-0a01210f5ef2 · outbound

This paper cites An improved contrastive learning network for semi-supervised multi-structure segmentation in echocardiography,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation An improved contrastive learning network for semi-supervised multi-structure segmentation in echocardiography,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.516435Z

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Observation a11b4d48-0068-49ec-9608-b63e302c8744 · outbound

This paper cites Deep residual learning for image recognition,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Deep residual learning for image recognition,

Reference 8

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Observation 351091fd-88e9-4a87-a297-38a81167084a · outbound

This paper cites Basic transthoracic echocardiography,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Basic transthoracic echocardiography,

Reference 9

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Observation e321fbb3-9085-40b9-b491-42bbf8cb4603 · outbound

This paper cites Deep learning for segmentation using an open large-scale dataset in 2d echocardiography,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Deep learning for segmentation using an open large-scale dataset in 2d echocardiography,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.470444Z

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Observation 98a3d2ff-ef97-4bfa-b49f-5c133b856e9c · outbound

This paper cites Shape-aware semi-supervised 3d seman- tic segmentation for medical images,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Shape-aware semi-supervised 3d seman- tic segmentation for medical images,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.452089Z

Source-reported events for the cited work

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Observation 1aa8a986-d12e-4119-9d34-d3e434e32915 · outbound

This paper cites Edmae: An efficient decoupled masked autoen- coder for standard view identification in pediatric echocardiography,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Edmae: An efficient decoupled masked autoen- coder for standard view identification in pediatric echocardiography,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.434734Z

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Observation fae1451c-2904-4f2a-b20a-00f5bfdaa479 · outbound

This paper cites Semi-supervised medical image segmentation through dual-task consistency,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Semi-supervised medical image segmentation through dual-task consistency,

Reference 13

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Observation 3402429b-f569-4b45-a900-9bb97e3fc349 · outbound

This paper cites Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency,

Reference 14

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Observation 1614952d-842c-4125-bc79-769604ce6a3c · outbound

This paper cites Deep echocar- diography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Deep echocar- diography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.398439Z

Source-reported events for the cited work

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Observation 8ef7b2a3-4492-4fd0-833e-50e313db9db4 · outbound

This paper cites Classmix: Segmentation-based data augmentation for semi-supervised learning,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Classmix: Segmentation-based data augmentation for semi-supervised learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.382277Z

Source-reported events for the cited work

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Observation 4fe9a5d0-4e2a-42b3-8de1-2819660e7a18 · outbound

This paper cites Semi-supervised semantic segmen- tation with cross-consistency training,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Semi-supervised semantic segmen- tation with cross-consistency training,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.365080Z

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Observation 70e21f4c-2297-416f-bc0c-2b40a69db0f3 · outbound

This paper cites Echocar- diography segmentation with enforced temporal consistency,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Echocar- diography segmentation with enforced temporal consistency,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.348758Z

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Observation aa1374a4-4b2d-42ec-ba1d-c6a1a321047f · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 19

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Observation 835f14de-bc53-4b30-8b63-6920de887bc3 · outbound

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A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Unresolved cited work

Reference 20

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Observation a86c8e82-3823-4960-abe8-167edd879f29 · outbound

This paper cites Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 21

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Observation 369d34e4-339a-4b79-8f33-33cb9e211222 · outbound

This paper cites When cnn meet with vit: Towards semi-supervised learning for multi-class medical image semantic segmentation,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation When cnn meet with vit: Towards semi-supervised learning for multi-class medical image semantic segmentation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.301333Z

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Observation e2b79592-206c-4ade-a8cd-a667386d8ab4 · outbound

This paper cites Semi-supervised segmentation of echocardiography videos via noise-resilient spatiotem- poral semantic calibration and fusion,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Semi-supervised segmentation of echocardiography videos via noise-resilient spatiotem- poral semantic calibration and fusion,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.284877Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1d960a11-62d5-499d-b8f0-26dff0b07dc7 · outbound

This paper cites Semi-supervised left atrium segmentation with mutual consistency training,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Semi-supervised left atrium segmentation with mutual consistency training,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.268399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 82cef72a-f5e1-4bb3-928c-707068499c01 · outbound

This paper cites Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T05:08:42.154539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bda96cd2-1ecb-4c53-8cf6-810215935435 · outbound

This paper cites Boundary attention with multi-task consistency constraints for semi-supervised 2d echocar- diography segmentation,.

A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation Boundary attention with multi-task consistency constraints for semi-supervised 2d echocar- diography segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:08:42.237657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.