Paper Citation Record · LEDGER

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation

As of 13 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 3 inbound Pith citation observations for arXiv:2411.13147.

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

pith.paper-citation-record.v1
2411.13147 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

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measured 60 of 60 standing notices

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:18:09.803305Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T13:54:22.084298Z

Reference resolution

57 of 57 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0799ff41-015c-4f04-9bbe-c28bf94096df · outbound

This paper cites Slic superpixels compared to state-of-the-art superpixel methods.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Slic superpixels compared to state-of-the-art superpixel methods

Reference 1

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Observation f34d9660-d363-411e-a4f7-ed92cc1cfb7d · outbound

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

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Bidirectional copy-paste for semi-supervised medical image segmentation

Reference 2

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Observation 2879b140-3e55-44d6-99ee-346f64955715 · outbound

This paper cites Semi-supervised clustering methods.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Semi-supervised clustering methods

Reference 3

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Observation 9e63ad45-ad64-4c22-8e21-636ab6a466d9 · outbound

This paper cites Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? TMI, 37(11):2514–2525, 2018.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? TMI, 37(11):2514–2525, 2018

Reference 4

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Observation 7edf3b3d-275a-4133-a773-9a7c28fead3a · outbound

This paper cites Computed tomography.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Computed tomography

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.338717Z

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Observation 80972e1b-0aa4-4911-b55c-c47c09ce8c94 · outbound

This paper cites Adaptive bidirectional displacement for semi-supervised medical image segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Adaptive bidirectional displacement for semi-supervised medical image segmentation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.322789Z

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Observation 116e6c6e-a0f4-4c33-b931-514619e623c2 · outbound

This paper cites Graph-based semi-supervised learning: A review.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Graph-based semi-supervised learning: A review

Reference 7

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Observation 6ce2b2db-3e8b-4ad0-8a25-3e31a6f93320 · outbound

This paper cites Domain adaptation for med- ical image segmentation using transformation-invariant self- training.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Domain adaptation for med- ical image segmentation using transformation-invariant self- training

Reference 8

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Observation 381ca390-ff3e-4257-847f-f224a60c22ea · outbound

This paper cites Overview of functional magnetic resonance imaging.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Overview of functional magnetic resonance imaging

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.278091Z

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Observation b8e1147f-b550-4205-a9c8-af73bd383d74 · outbound

This paper cites A new model for learning in graph domains.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation A new model for learning in graph domains

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.261865Z

Source-reported events for the cited work

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Observation 1eefc941-7f88-4149-9fee-3fe746a51839 · outbound

This paper cites Unsupervised clustering using pseudo- semi-supervised learning.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Unsupervised clustering using pseudo- semi-supervised learning

Reference 11

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Observation a0e30f2b-5ca7-4305-9f27-3c8a0d3b0e22 · outbound

This paper cites Semicvt: Semi-supervised convolutional vi- sion transformer for semantic segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Semicvt: Semi-supervised convolutional vi- sion transformer for semantic segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.237126Z

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Observation 529f58a0-dade-4480-9151-66a0bd50790e · outbound

This paper cites Detection recovery in online multi-object track- ing with sparse graph tracker.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Detection recovery in online multi-object track- ing with sparse graph tracker

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.224902Z

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Observation 074863ba-48d6-4eaf-99ef-2223f37f9a66 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Adam: A Method for Stochastic Optimization

Reference 14

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Observation 760b9f16-b8c3-46c2-a883-c9dbb76c8df2 · outbound

This paper cites Semi-supervised classifi- cation with graph convolutional networks.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Semi-supervised classifi- cation with graph convolutional networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.210304Z

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Observation 82e3f72e-a70b-443b-99ba-205c014bf1ae · outbound

This paper cites Distance metric learning using graph convolutional networks: Appli- cation to functional brain networks.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Distance metric learning using graph convolutional networks: Appli- cation to functional brain networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.197088Z

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Observation 8725719a-6896-47d0-85db-fe09f4feb485 · outbound

This paper cites Graph networks for multiple object tracking.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Graph networks for multiple object tracking

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.183366Z

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Observation 04cb3867-1ed4-443c-9cfa-b91cbcde7793 · outbound

This paper cites Shape-aware semi-supervised 3d semantic segmentation for medical im- ages.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Shape-aware semi-supervised 3d semantic segmentation for medical im- ages

Reference 18

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Observation 874f33db-51fb-4039-9008-e680cb05e186 · outbound

This paper cites Semi-supervised clustering in attributed het- erogeneous information networks.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Semi-supervised clustering in attributed het- erogeneous information networks

Reference 19

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Observation 5f204c97-0936-4788-b3c8-37abe4a63898 · outbound

This paper cites Semi-supervised clustering with deep metric learning and graph embedding.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Semi-supervised clustering with deep metric learning and graph embedding

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.139317Z

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Observation 3ec96808-94c9-4486-8473-22280f68a65f · outbound

This paper cites Towards deeper graph neural networks.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Towards deeper graph neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.122637Z

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Observation f9954df1-efda-43aa-b625-9c0792923e55 · outbound

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

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Semi-supervised medical image segmentation through dual- task consistency

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.106846Z

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Observation 2b07d7b0-b49a-44d9-a1bb-be2ef500cd47 · outbound

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

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via un- certainty rectified pyramid consistency

Reference 23

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Observation 6b34fef7-84a9-45f9-8ada-55a8c1badd09 · outbound

This paper cites Gcan: Graph convolutional adversarial network for unsupervised domain adaptation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Gcan: Graph convolutional adversarial network for unsupervised domain adaptation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.069121Z

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Observation c299e05f-4bce-47d5-ad4d-bc1da63562bf · outbound

This paper cites Demystifying struc- tural disparity in graph neural networks: Can one size fit all? NeurIPS, 36, 2024.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Demystifying struc- tural disparity in graph neural networks: Can one size fit all? NeurIPS, 36, 2024

Reference 25

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Observation 63b242f2-b5bf-499f-ae2c-dbe767dbf636 · outbound

This paper cites Caussl: Causality-inspired semi-supervised learning for medical image segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Caussl: Causality-inspired semi-supervised learning for medical image segmentation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.027533Z

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Observation d4336b71-e0a6-4a67-bbca-f6d852ac5c97 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:09.006177Z

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Observation 78a7450c-5526-44d4-9196-9646b5096a7e · outbound

This paper cites Graphnet: Learning image pseudo annotations for weakly- supervised semantic segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Graphnet: Learning image pseudo annotations for weakly- supervised semantic segmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.988534Z

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Observation 90f27de3-cf90-48ba-8d7d-6beff924058e · outbound

This paper cites Re- search progress on semi-supervised clustering.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Re- search progress on semi-supervised clustering

Reference 29

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Observation 90e1fdaa-b829-4a71-b2f3-90a2720bac50 · outbound

This paper cites Efficient 3d semantic segmentation with superpoint transformer.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Efficient 3d semantic segmentation with superpoint transformer

Reference 30

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Observation 0621cec1-065d-469f-8294-a6f689007bcb · outbound

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

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.932102Z

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Observation b1076fa3-727b-48e1-ab71-1ee729630a84 · outbound

This paper cites Deeporgan: Multi-level deep convolutional networks for automated pan- creas segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Deeporgan: Multi-level deep convolutional networks for automated pan- creas segmentation

Reference 32

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Observation e729578b-81f1-4db4-b332-13c29c3317fb · outbound

This paper cites The graph neural net- work model.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation The graph neural net- work model

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.896207Z

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Observation a27b4bdf-0281-4f9f-8b2f-e7dac6343add · outbound

This paper cites Modeling re- lational data with graph convolutional networks.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Modeling re- lational data with graph convolutional networks

Reference 34

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Observation 06d071f3-408e-49f8-9e68-60d087544065 · outbound

This paper cites Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.859534Z

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Observation 8ba660b6-64a8-4cac-b20f-eb57d8f027ce · outbound

This paper cites Graphalign: Enhancing accurate feature alignment by graph matching for multi-modal 3d object detection.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Graphalign: Enhancing accurate feature alignment by graph matching for multi-modal 3d object detection

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.842217Z

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Observation 6a48821a-623b-4f34-ae17-680c25e6512b · outbound

This paper cites Corrmatch: Label propagation via correlation matching for semi-supervised semantic segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Corrmatch: Label propagation via correlation matching for semi-supervised semantic segmentation

Reference 37

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Observation e4012e71-c45b-4f64-8b54-55dcbd5766f8 · outbound

This paper cites Spatial context-aware self-attention model for multi-organ segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Spatial context-aware self-attention model for multi-organ segmentation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.806863Z

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Observation fd9dc3b1-52eb-4af0-a91d-a176c2b8f8cf · outbound

This paper cites High-resolution 3d abdominal segmentation with random patch network fusion.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation High-resolution 3d abdominal segmentation with random patch network fusion

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.787725Z

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Observation d6eab74e-2d31-4480-baa4-5eedd067c043 · outbound

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

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.767273Z

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Observation 9e76b369-135d-4dd1-96a1-8cf706feeb15 · outbound

This paper cites Advent: Adversarial entropy mini- mization for domain adaptation in semantic segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Advent: Adversarial entropy mini- mization for domain adaptation in semantic segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.747901Z

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Observation e31f24d7-9d93-4423-9ee8-0a05e54a6770 · outbound

This paper cites Towards generic semi- supervised framework for volumetric medical image seg- mentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Towards generic semi- supervised framework for volumetric medical image seg- mentation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.724723Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 27793f7b-423d-47f4-ba28-681bab740eff · outbound

This paper cites Semi-supervised learning by augmented distribution alignment.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Semi-supervised learning by augmented distribution alignment

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.697680Z

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Observation ed027ecd-b4d5-42f7-be0e-4d7a8bcf9686 · outbound

This paper cites Contrastmask: Contrastive learn- ing to segment every thing.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Contrastmask: Contrastive learn- ing to segment every thing

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.677012Z

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Observation d8e630e3-2f15-437f-be71-946f12f1f211 · outbound

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

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Semi-supervised left atrium segmentation with mutual consistency training

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.661310Z

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Observation d03488e5-53de-4519-b34b-2b011c7afceb · outbound

This paper cites Mutual consistency learning for semi-supervised medical image segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Mutual consistency learning for semi-supervised medical image segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.646031Z

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Observation d0affba0-5c7e-41c7-b3b0-246881e06848 · outbound

This paper cites Exploring smoothness and class-separation for semi-supervised medical image segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Exploring smoothness and class-separation for semi-supervised medical image segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.626873Z

Source-reported events for the cited work

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Observation befc612b-8fc6-4565-b4ae-a2414a51c02b · outbound

This paper cites A global benchmark of algo- rithms for segmenting the left atrium from late gadolinium- enhanced cardiac magnetic resonance imaging.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation A global benchmark of algo- rithms for segmenting the left atrium from late gadolinium- enhanced cardiac magnetic resonance imaging

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.608402Z

Source-reported events for the cited work

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Observation 6397d595-6418-442e-9c94-aabae8b86e8a · outbound

This paper cites Spatial tempo- ral graph convolutional networks for skeleton-based action recognition.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Spatial tempo- ral graph convolutional networks for skeleton-based action recognition

Reference 49

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Observation c9435b32-2179-4f78-95e9-1f14957431b5 · outbound

This paper cites Rethinking semi-supervised medical image segmentation: A variance-reduction perspective.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Rethinking semi-supervised medical image segmentation: A variance-reduction perspective

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.571348Z

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Observation dffc3711-85c5-483d-8d99-5e5946a9c9bf · outbound

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

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.553555Z

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Observation b7d13a06-e6f2-4fee-b13f-7a35c13b8637 · outbound

This paper cites Scalable semi-supervised clustering via structural entropy with different constraints.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Scalable semi-supervised clustering via structural entropy with different constraints

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.528244Z

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Observation ed9e67fb-a4cc-4687-967f-df244f018ff5 · outbound

This paper cites Decoupling the depth and scope of graph neural networks.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Decoupling the depth and scope of graph neural networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.509666Z

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Observation 64b8c24a-85ea-48e6-bc96-fcfa6d52121e · outbound

This paper cites Affinity attention graph neural network for weakly supervised semantic segmentation.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Affinity attention graph neural network for weakly supervised semantic segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.489422Z

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Observation 182b4b80-b7ad-445d-a762-728e0eb42d2e · outbound

This paper cites Deep adversarial net- works for biomedical image segmentation utilizing unanno- tated images.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Deep adversarial net- works for biomedical image segmentation utilizing unanno- tated images

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.470837Z

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Observation 038a768a-79c6-4923-9175-f3512d4d285b · outbound

This paper cites Graphfpn: Graph feature pyramid network for object detection.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Graphfpn: Graph feature pyramid network for object detection

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.447794Z

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Observation b654748c-88b2-420d-8405-b3e152f3773f · outbound

This paper cites Graph attention guid- ance network with knowledge distillation for semantic seg- mentation of remote sensing images.

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation Graph attention guid- ance network with knowledge distillation for semantic seg- mentation of remote sensing images

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:53:08.428083Z

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Pith citing papers

Observation 969c0b1d-d655-44a0-8279-abaa58e33fb6 · inbound

In-context learning for medical image segmentation cites this paper.

In-context learning for medical image segmentation GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T13:18:09.803305Z

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Observation 29a37a35-4531-4a68-837d-951f5986d9fe · inbound

TransMedSeg: A Transferable Semantic Framework for Semi-Supervised Medical Image Segmentation cites this paper.

TransMedSeg: A Transferable Semantic Framework for Semi-Supervised Medical Image Segmentation GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:34.601215Z

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Observation f88ccc2c-5a77-4437-889a-a45ed1728dc2 · inbound

UniCT Depth: Event-Image Fusion Based Monocular Depth Estimation with Convolution-Compensated ViT Dual SA Block cites this paper.

UniCT Depth: Event-Image Fusion Based Monocular Depth Estimation with Convolution-Compensated ViT Dual SA Block GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:54:22.180959Z

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