Paper Citation Record · LEDGER

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation

As of 13 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2412.15380.

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

pith.paper-citation-record.v1
2412.15380 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:31:30.193667Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

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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

38 of 38 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4708784-2e89-4003-8714-a672d6065c22 · outbound

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

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 1

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Observation d6296dbe-3424-44f4-8132-d859745e874f · outbound

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

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 2

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Observation 7c3432a9-178c-479a-8dc1-7b5ed8c85dbc · outbound

This paper cites A survey on semi-supervised learning.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation A survey on semi-supervised learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:31.465418Z

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Observation f03c8a71-c7a9-48c5-8762-a200360d4fd4 · outbound

This paper cites Mixmatch: A holistic approach to semi-supervised learning.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Mixmatch: A holistic approach to semi-supervised learning

Reference 4

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Observation de4eed77-3fda-4a70-af0d-e9b709b5da57 · outbound

This paper cites Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning

Reference 5

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Observation efe8f9b0-faa3-441b-80df-ad7a52339982 · outbound

This paper cites Smooth neighbors on teacher graphs for semi-supervised learning.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Smooth neighbors on teacher graphs for semi-supervised learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:31.389337Z

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Observation 1114a800-78c1-4f77-bc98-19e89aa677f3 · outbound

This paper cites Instance localization for self-supervised detection pretraining.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Instance localization for self-supervised detection pretraining

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:31.366734Z

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Observation cda4bfcf-9a10-4364-9277-f820dabf90fb · outbound

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

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 8

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This paper cites Unsupervised data augmentation for consistency training.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Unsupervised data augmentation for consistency training

Reference 9

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Observation 77fb4d53-18d7-496d-bd09-ae1d2af37131 · outbound

This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

Reference 10

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Observation 7f65d679-faf0-49fb-8086-ebc2e5f15f1b · outbound

This paper cites Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:31.270896Z

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Observation 50a51ff0-f0c0-454a-b8cf-83577191e1f5 · outbound

This paper cites Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation

Reference 12

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Observation a76eaccd-1985-4330-b1f0-bf171349d973 · outbound

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

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:31.232177Z

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Observation f2c9e07c-5da5-42bb-8e08-e419ff920269 · outbound

This paper cites Mcf: Mutual correction framework for semi-supervised medical image segmentation.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Mcf: Mutual correction framework for semi-supervised medical image segmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:31.195148Z

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Observation ac3bc52a-7620-43ef-a34a-b3cb016a5bb4 · outbound

This paper cites Semi-supervised semantic segmentation with cross pseudo supervision.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Semi-supervised semantic segmentation with cross pseudo supervision

Reference 15

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Observation cea8e6a7-7087-4e75-b0f9-8f4f34497e0d · outbound

This paper cites Selecting the best optimizers for deep learning--based medical image segmentation.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Selecting the best optimizers for deep learning--based medical image segmentation

Reference 16

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Observation b7ca1ab2-73a0-495e-ac86-7321e679b8de · outbound

This paper cites Deep learning-based detection and classification of bone lesions on staging computed tomography in prostate cancer: A development study.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Deep learning-based detection and classification of bone lesions on staging computed tomography in prostate cancer: A development study

Reference 17

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Observation 102a2d47-da86-43d3-809e-30641b386864 · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 18

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Observation 5f6bf903-db5e-4e14-b657-a0b034bb6da7 · outbound

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Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling

Reference 19

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Observation 6b4adaba-7562-465b-8123-c108e3e2141a · outbound

This paper cites Virtual adversarial training: a regularization method for supervised and semi-supervised learning.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Virtual adversarial training: a regularization method for supervised and semi-supervised learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:31.017454Z

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Observation 421de973-bd41-49e4-b95c-776aa1c51f23 · outbound

This paper cites How does disagreement help generalization against label corruption? In International conference on machine learning, pages 7164--7173.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation How does disagreement help generalization against label corruption? In International conference on machine learning, pages 7164--7173

Reference 21

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Observation da6dbfac-18c7-4404-a419-12f046c44294 · outbound

This paper cites Reference-guided pseudo-label generation for medical semantic segmentation.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Reference-guided pseudo-label generation for medical semantic segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:30.951553Z

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Observation 516699cf-a850-42c4-afd1-91d4dcb634f2 · outbound

This paper cites Interpolation consistency training for semi-supervised learning.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Interpolation consistency training for semi-supervised learning

Reference 23

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Observation 862c7c19-7831-4854-ab0c-ad59fff71c8c · outbound

This paper cites Tripled-uncertainty guided mean teacher model for semi-supervised medical image segmentation.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Tripled-uncertainty guided mean teacher model for semi-supervised medical image segmentation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:30.894322Z

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Observation 0ef6a61b-b86e-4e4b-9ea1-27e464c16184 · outbound

This paper cites Semi-supervised neuron segmentation via reinforced consistency learning.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Semi-supervised neuron segmentation via reinforced consistency learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:30.853564Z

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This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 26

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This paper cites Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:30.797112Z

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Observation 4f4463ed-202f-4982-9f63-00344cb74a79 · outbound

This paper cites Self-supervised learning for organs at risk and tumor segmentation with uncertainty quantification.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Self-supervised learning for organs at risk and tumor segmentation with uncertainty quantification

Reference 28

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Observation 8678a6bb-3817-4313-b103-e907c5fdadb6 · outbound

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

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 29

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

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Shape-aware semi-supervised 3d semantic segmentation for medical images

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:30.674926Z

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Observation a1e5de94-edc3-4ae6-90bd-a5996f2991a5 · outbound

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

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Semi-supervised left atrium segmentation with mutual consistency training

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:30.615731Z

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Observation e495da67-e3a7-4f5c-bf36-dd66f52b2bb7 · outbound

This paper cites Cr-sam: Curvature regularized sharpness-aware minimization.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Cr-sam: Curvature regularized sharpness-aware minimization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:30.580281Z

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

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:30.546402Z

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Observation 7fc77fbc-dd40-4f87-843a-576b9c229d73 · outbound

This paper cites Ms-net: Multi-site network for improving prostate segmentation with heterogeneous mri data.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Ms-net: Multi-site network for improving prostate segmentation with heterogeneous mri data

Reference 34

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Observation 0d351c3e-b2a6-446d-bb25-4efa371415ec · outbound

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

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmentation through dual-task consistency

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:30.485181Z

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Observation 5766e66e-37a2-468f-9bac-e97712d31be5 · outbound

This paper cites Pseudo-label guided contrastive learning for semi-supervised medical image segmentation.

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Pseudo-label guided contrastive learning for semi-supervised medical image segmentation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:30.445262Z

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

Observation 38b1673b-4a30-41be-9c5c-2ed7547ac86c · outbound

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

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Caussl: Causality-inspired semi-supervised learning for medical image segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:30.392106Z

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Observation 6e5d58eb-dea9-47be-96cb-ee3d02f3628b · outbound

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

Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:31:30.338418Z

Source-reported events for the cited work

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

Pith citing papers

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