Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation | Paper Citation Record

Observation 9033dca0-f12d-470c-a8b3-89f2acb330e6 · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation V-Net: Fully convolutional neural networks for volumetric medical image segmentation,

Reference 1

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Observation 6fac96af-14a0-4daa-b711-a89d42e3c7ff · outbound

This paper cites Grouping boundary proposals for fast interactive image segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Grouping boundary proposals for fast interactive image segmentation,

Reference 2

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Observation e10d11b8-550f-4a3e-94f2-5594914efc2d · outbound

This paper cites Towards robust referring image segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Towards robust referring image segmentation,

Reference 4

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Observation 2f6f7b01-2130-444f-baea-6b8bf1bdaf15 · outbound

This paper cites Bilateral context modeling for residual coding in lossless 3d medical image compression,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Bilateral context modeling for residual coding in lossless 3d medical image compression,

Reference 5

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Observation 51dedceb-7b9b-4f3f-859a-2c0af8e1b43e · outbound

This paper cites Mm-net: A mixformer-based multi-scale network for anatomical and functional image fusion,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Mm-net: A mixformer-based multi-scale network for anatomical and functional image fusion,

Reference 6

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Observation 23b23d38-48ad-474d-9676-72a70fb72bda · outbound

This paper cites Anomaly detection for medical images using heterogeneous auto-encoder,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Anomaly detection for medical images using heterogeneous auto-encoder,

Reference 7

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Observation 906e4492-da75-45cd-a69d-59164be075cc · outbound

This paper cites Mas-cl: An end- to-end multi-atlas supervised contrastive learning framework for brain roi segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Mas-cl: An end- to-end multi-atlas supervised contrastive learning framework for brain roi segmentation,

Reference 8

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Observation 9ac18aad-a71e-4e65-b5a0-03a795d34dee · outbound

This paper cites Balancing multi- target semi-supervised medical image segmentation with collaborative generalist and specialists,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Balancing multi- target semi-supervised medical image segmentation with collaborative generalist and specialists,

Reference 9

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Observation 18290246-296d-4246-9e0b-3667c1001812 · outbound

This paper cites Weakmedsam: Weakly-supervised medical image segmentation via sam with sub-class exploration and prompt affinity mining,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Weakmedsam: Weakly-supervised medical image segmentation via sam with sub-class exploration and prompt affinity mining,

Reference 10

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Observation eb430eb9-15c0-4aff-ac4e-7c25cace07b1 · outbound

This paper cites Stitching, fine-tuning, re-training: A sam-enabled framework for semi-supervised 3d medical image segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Stitching, fine-tuning, re-training: A sam-enabled framework for semi-supervised 3d medical image segmentation,

Reference 11

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Observation 3ce39bd2-c2ef-4fcc-bb40-13bb80bfce72 · outbound

This paper cites Constructing and exploring intermediate domains in mixed domain semi-supervised med- ical image segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Constructing and exploring intermediate domains in mixed domain semi-supervised med- ical image segmentation,

Reference 12

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Observation bfa94cb2-3694-4548-b3b2-08d13e5cd028 · outbound

This paper cites Semirs-coc: Semi- supervised classification for complex remote sensing scenes with cross- object consistency,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semirs-coc: Semi- supervised classification for complex remote sensing scenes with cross- object consistency,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:06.966628Z

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Observation 341847d9-9512-484d-908b-805c32a71bfb · outbound

This paper cites Hybrid perturba- tion strategy for semi-supervised crowd counting,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Hybrid perturba- tion strategy for semi-supervised crowd counting,

Reference 14

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Observation b6384bff-c9fb-41ae-9766-761f28abaaf0 · outbound

This paper cites Semi-supervised learning with heterogeneous distribution consistency for visible infrared person re-identification,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semi-supervised learning with heterogeneous distribution consistency for visible infrared person re-identification,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:06.678378Z

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Observation 215dc849-d71d-44b1-adc4-ab00842d1c32 · outbound

This paper cites ACPL: Anti-curriculum pseudo-labelling for semi-supervised medical image classification,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation ACPL: Anti-curriculum pseudo-labelling for semi-supervised medical image classification,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:06.499822Z

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Observation c0b4ce37-79b1-4d79-935d-dbea39cb801a · outbound

This paper cites Multi- modal contrastive mutual learning and pseudo-label re-learning for semi- supervised medical image segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Multi- modal contrastive mutual learning and pseudo-label re-learning for semi- supervised medical image segmentation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:06.343457Z

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Observation 36bbf524-3f01-4621-946c-2ba393ceb239 · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning,

Reference 18

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Observation e659012c-c220-4b34-ad9e-c274c1eb2637 · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Mutual consistency learning for semi-supervised medical image segmentation,

Reference 19

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Observation 20dd7c19-be32-434c-9e45-2a95f0eb3086 · outbound

This paper cites Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:06.067998Z

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Observation cc7effbc-eaf2-4cfb-a7d0-93ac66ccab95 · outbound

This paper cites Magicnet: Semi-supervised multi-organ segmentation via magic-cube partition and recovery,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Magicnet: Semi-supervised multi-organ segmentation via magic-cube partition and recovery,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:05.923078Z

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Observation 1bbc8e6b-6f32-41d3-9d95-0e7115459fab · outbound

This paper cites Cross-patch dense contrastive learning for semi-supervised segmentation of cellular nuclei in histopathologic images,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Cross-patch dense contrastive learning for semi-supervised segmentation of cellular nuclei in histopathologic images,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:05.763148Z

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Observation ec055d0c-0e98-482d-9963-6b40bf9ac79e · outbound

This paper cites Self-paced con- trastive learning for semi-supervised medical image segmentation with meta-labels,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Self-paced con- trastive learning for semi-supervised medical image segmentation with meta-labels,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:05.577911Z

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Observation 51bd8f2e-34c4-4020-823b-9f1ec335fd0f · outbound

This paper cites SSMD: Semi-supervised medical image detection with adaptive con- sistency and heterogeneous perturbation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation SSMD: Semi-supervised medical image detection with adaptive con- sistency and heterogeneous perturbation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:05.385898Z

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Observation 7a47e10b-a631-452e-a1cc-37cf54b86b42 · outbound

This paper cites Meta pseudo labels,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Meta pseudo labels,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:05.170150Z

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Observation 4a43b980-44eb-46b4-a5b9-cfe93a8b8ab3 · outbound

This paper cites SECRET: Self-consistent pseudo label refinement for unsupervised domain adaptive person re- identification,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation SECRET: Self-consistent pseudo label refinement for unsupervised domain adaptive person re- identification,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:04.995710Z

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Observation dba0a591-a20b-4017-ab98-a4fdec5fff60 · outbound

This paper cites Pro- tocon: Pseudo-label refinement via online clustering and prototypical consistency for efficient semi-supervised learning,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Pro- tocon: Pseudo-label refinement via online clustering and prototypical consistency for efficient semi-supervised learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:04.861175Z

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Observation 00bddd7f-b400-4f7b-ab3f-eee148c9cb0f · outbound

This paper cites Semi-supervised image de- raining using gaussian processes,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semi-supervised image de- raining using gaussian processes,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:04.713469Z

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Observation 113ac794-df87-4e01-8e8c-87c5ea668e90 · outbound

This paper cites Cmos-gan: Semi-supervised generative adversarial model for cross-modality face image synthesis,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Cmos-gan: Semi-supervised generative adversarial model for cross-modality face image synthesis,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:04.528602Z

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Observation ae44a70b-97b2-4473-a000-aeff88b9bf26 · outbound

This paper cites In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning

Reference 30

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Observation 7c284aa8-171b-4523-bfd5-b0eebff9aea4 · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation MixMatch: A holistic approach to semi-supervised learning,

Reference 31

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Observation 373faa22-c20f-4c45-83b0-78e96046b551 · outbound

This paper cites Adversarial dense contrastive learning for semi-supervised semantic segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Adversarial dense contrastive learning for semi-supervised semantic segmentation,

Reference 32

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Observation 24ad7da6-888a-41da-a879-8bd6d520aef0 · outbound

This paper cites Otamatch: Optimal transport assignment with pseudonce for semi-supervised learn- ing,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Otamatch: Optimal transport assignment with pseudonce for semi-supervised learn- ing,

Reference 33

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Observation c11bef6a-9aa0-4e62-a5d5-099c79100895 · outbound

This paper cites Distilling self-supervised vision transformers for weakly-supervised few-shot classification & segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Distilling self-supervised vision transformers for weakly-supervised few-shot classification & segmentation,

Reference 34

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Observation 3eae4fdd-89e8-413a-92fc-e40cc6582264 · outbound

This paper cites Learning self-supervised low-rank network for single-stage weakly and semi-supervised semantic segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Learning self-supervised low-rank network for single-stage weakly and semi-supervised semantic segmentation,

Reference 35

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Observation e9a25a3c-e9e8-4f3b-a08f-b152a07933cc · outbound

This paper cites A multi- task mean teacher for semi-supervised shadow detection,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation A multi- task mean teacher for semi-supervised shadow detection,

Reference 36

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Observation d0f9d8fa-320a-442d-91b8-9b1f9309f7a3 · outbound

This paper cites Semi- supervised learning of semantic correspondence with pseudo-labels,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semi- supervised learning of semantic correspondence with pseudo-labels,

Reference 37

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Observation 0f3eedad-182b-45bb-89ee-db6b21819ed0 · outbound

This paper cites Learning privacy-preserving student networks via discriminative-generative distillation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Learning privacy-preserving student networks via discriminative-generative distillation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:02.930050Z

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Observation fb7eb71c-ec27-4281-b7b6-bebfc987fb75 · outbound

This paper cites Semi-supervised semantic segmentation with high and low-level consistency,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semi-supervised semantic segmentation with high and low-level consistency,

Reference 39

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Observation 51cb993d-54fd-40cf-83b6-bef717980302 · outbound

This paper cites Learning from pixel-level label noise: A new perspective for semi-supervised semantic segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Learning from pixel-level label noise: A new perspective for semi-supervised semantic segmentation,

Reference 40

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Observation 76a6d756-da9e-4d97-b278-f9a7bf592b83 · outbound

This paper cites Sample- centric feature generation for semi-supervised few-shot learning,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Sample- centric feature generation for semi-supervised few-shot learning,

Reference 41

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Observation ec62fc22-df45-4d3c-ae9f-7895afb4870b · outbound

This paper cites Conflict- based cross-view consistency for semi-supervised semantic segmenta- tion,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Conflict- based cross-view consistency for semi-supervised semantic segmenta- tion,

Reference 42

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Observation 9d479e35-140b-416f-8e1d-b8b7841cbf5c · outbound

This paper cites Ssl++: Im- proving self-supervised learning by mitigating the proxy task-specificity problem,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Ssl++: Im- proving self-supervised learning by mitigating the proxy task-specificity problem,

Reference 43

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Observation f227fdef-aa51-48c6-90e7-0d271e071f80 · outbound

This paper cites Semi-supervised med- ical image classification with relation-driven self-ensembling model,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semi-supervised med- ical image classification with relation-driven self-ensembling model,

Reference 44

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Observation bef693d3-523c-472c-9b02-eb993883832c · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semi-supervised neuron segmentation via reinforced consistency learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:01.462641Z

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Observation 1429882a-d64c-4f9e-bdfc-4fc4ff3fa881 · outbound

This paper cites Generative consistency for semi-supervised cerebrovascular segmentation from TOF-MRA,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Generative consistency for semi-supervised cerebrovascular segmentation from TOF-MRA,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:01.246169Z

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Observation d3172f4d-2e49-4756-b77c-c0ade25149d5 · outbound

This paper cites Efficient semi-supervised gross target vol- ume of nasopharyngeal carcinoma segmentationvia uncertainty rectified pyramid consistency,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Efficient semi-supervised gross target vol- ume of nasopharyngeal carcinoma segmentationvia uncertainty rectified pyramid consistency,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:01.015706Z

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Observation ad3f229c-739e-47b9-92fe-64258f49041f · outbound

This paper cites Dual-consistency semi- supervised learning with uncertainty quantification for covid-19 lesion segmentation from CT Images,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Dual-consistency semi- supervised learning with uncertainty quantification for covid-19 lesion segmentation from CT Images,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:00.666426Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2d0e7f05-458a-4a95-86dd-3d5f2e4365f3 · outbound

This paper cites Semi-supervised task-driven data augmentation for medical image segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semi-supervised task-driven data augmentation for medical image segmentation,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:00.423347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 22efa0fb-5729-4997-b2e9-3f9cf0ed4f9b · outbound

This paper cites Semi-supervised medical image segmentation with voxel stability and reliability constraints,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmentation with voxel stability and reliability constraints,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:00.212609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 59719d89-2a15-40fe-aa04-8aa048b52777 · outbound

This paper cites Boostmis: Boosting medical image semi-supervised learning with adaptive pseudo labeling and informative active annotation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Boostmis: Boosting medical image semi-supervised learning with adaptive pseudo labeling and informative active annotation,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:59.917811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 42b98818-c1c8-4e34-b484-bd1eb64c1b0b · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmentation through dual-task consistency,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:59.685205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 65d8052d-4b61-4966-85f2-46a55f717e0d · outbound

This paper cites A multi-view co-training network for semi-supervised medical image-based prognostic prediction,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation A multi-view co-training network for semi-supervised medical image-based prognostic prediction,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:59.488261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 97eac403-8e95-45e0-af06-6630b3aaee3b · outbound

This paper cites Transformation-consistent self-ensembling model for semisupervised medical image segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Transformation-consistent self-ensembling model for semisupervised medical image segmentation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:59.417915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7bba94d1-e56b-4d27-839b-5221ecb0a260 · outbound

This paper cites Uncertainty- guided dual-views for semi-supervised volumetric medical image seg- mentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Uncertainty- guided dual-views for semi-supervised volumetric medical image seg- mentation,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:59.223111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8f38e9ec-ed50-4230-a55d-bb14a0c6c577 · outbound

This paper cites MutexMatch: Semi-supervised learning with mutex-based consistency regularization,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation MutexMatch: Semi-supervised learning with mutex-based consistency regularization,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:59.086177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fa12743b-6169-495b-b032-f2c51a0936c7 · outbound

This paper cites Semi-supervised semantic segmentation using unreliable pseudo-labels,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semi-supervised semantic segmentation using unreliable pseudo-labels,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:58.910883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d7bff7ba-f230-45fa-a1eb-28c02638de1e · outbound

This paper cites Fine-grained background representation for weakly supervised semantic segmenta- tion,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Fine-grained background representation for weakly supervised semantic segmenta- tion,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:58.726300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b25625f0-3941-4319-9c21-8fc9404ab7aa · outbound

This paper cites Background activation suppression for weakly supervised object localization and semantic segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Background activation suppression for weakly supervised object localization and semantic segmentation,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:58.546661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 59344a2c-9fc4-4ca7-a3e0-1d54658254f4 · outbound

This paper cites Spatial structure constraints for weakly supervised semantic segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Spatial structure constraints for weakly supervised semantic segmentation,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:55.318607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 090cd49b-add6-4003-aa75-b010266b49e4 · outbound

This paper cites Weakly supervised semantic segmentation by pixel-to-prototype contrast,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Weakly supervised semantic segmentation by pixel-to-prototype contrast,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:58.379166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d5374f7e-3529-4ff6-8cf1-6981b74ca424 · outbound

This paper cites C 2 am: Contrastive learning of class-agnostic activation map for weakly super- vised object localization and semantic segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation C 2 am: Contrastive learning of class-agnostic activation map for weakly super- vised object localization and semantic segmentation,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:58.265172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9d83ff12-ed4a-4acb-8411-2b7052f11986 · outbound

This paper cites Foundation model assisted weakly supervised semantic segmentation,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Foundation model assisted weakly supervised semantic segmentation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:58.061236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4414d65d-cb11-4508-8651-96a38b0ce435 · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Bidirectional copy-paste for semi-supervised medical image segmentation,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:57.949155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 25ac26ea-969c-4959-baa1-9c674f9dbea2 · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework 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 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:57.783843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 01a7e702-f739-4b00-a84e-a975155378b0 · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation MCF: Mutual correction framework for semi-supervised medical image segmentation,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:57.672272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 318c60bc-b5b4-45e3-913c-ee420df9bfd5 · outbound

This paper cites The cancer imaging archive (TCIA): Maintaining and operating a public information repository,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation The cancer imaging archive (TCIA): Maintaining and operating a public information repository,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:57.497033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8a8ae1d7-90b1-483d-97dd-b9b8bb95d5c5 · outbound

This paper cites Deep learning techniques for automatic mri cardiac multi- structures segmentation and diagnosis: Is the problem solved?,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Deep learning techniques for automatic mri cardiac multi- structures segmentation and diagnosis: Is the problem solved?,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:57.398996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 55b902b4-a96f-4585-8682-250440713f73 · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Exploring smoothness and class-separation for semi-supervised medical image segmentation,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:57.237687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c0163dd5-46cb-4d10-9e75-62e30b454799 · outbound

This paper cites Retinal vessel segmentation by improved matched filtering: Evaluation on a new high-resolution fundus image database,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Retinal vessel segmentation by improved matched filtering: Evaluation on a new high-resolution fundus image database,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:57.099905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f54e3b4a-bca6-47a7-9125-a12452889108 · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:56.962756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5b8eaef9-9a9e-427a-bbac-44672a8ea8af · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Shape-aware semi-supervised 3d semantic segmentation for medical images,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:56.798668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b52af452-55ab-4b22-b923-0e1ce08907d7 · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Semi-supervised left atrium segmentation with mutual consistency training,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:56.683431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c4631753-3b88-4b53-acb9-f07dccaef41a · outbound

This paper cites Segment anything,.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Segment anything,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:56.532218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 985f866f-b114-42c8-abb0-cec49a13e53c · outbound

This paper cites SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:56.157284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7b23413-5703-420d-a851-38748b5f2f6d · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation Temporal Ensembling for Semi-Supervised Learning

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:56.211332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9443903-cb8f-465f-a93e-b6a018ba2335 · outbound

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

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation CauSSL: Causality- inspired semi-supervised learning for medical image segmentation,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:56.458234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f86a7e7b-ea5d-4071-aa4d-3c0968f9768e · outbound

This paper cites SAM-Med2D.

Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation SAM-Med2D

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:56.309193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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