Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation | Paper Citation Record

Observation ca14add7-c88b-41e9-bc23-985b9e69fe13 · outbound

This paper cites Deep adversarial networks for biomedical image segmentation utilizing unannotated images,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Deep adversarial networks for biomedical image segmentation utilizing unannotated images,

Reference 1

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Observation e4bfee80-6923-49ea-98b1-3061b1ca66ce · outbound

This paper cites Uncertainty-aware hierarchical aggregation network for medical image segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Uncertainty-aware hierarchical aggregation network for medical image segmentation,

Reference 2

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Observation 3f242b24-c176-45bf-841b-fa6de6eca5bf · outbound

This paper cites Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation,

Reference 3

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Observation 71fb1b34-ae8b-432e-95c3-52ddd177142e · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Bidirectional copy- paste for semi-supervised medical image segmentation,

Reference 4

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Observation 67fb9ecb-f51f-486e-9da7-ffc14bae9414 · outbound

This paper cites Correlation-aware mutual learning for semi-supervised medical image segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Correlation-aware mutual learning for semi-supervised medical image segmentation,

Reference 5

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Observation 7b58126c-bb23-4eb0-bbad-83f81d4f4849 · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmentation through dual-task consistency,

Reference 6

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Observation c065fa25-30b9-4c17-83f0-e9a8f9586063 · outbound

This paper cites Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency,

Reference 7

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Observation 5f14b10f-6773-41c7-b564-4e10a89a85bd · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Semi-supervised left atrium segmentation with mutual consistency training,

Reference 8

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Observation f8a96885-3628-47fd-9f44-d8239d0bff78 · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Mcf: Mutual correction framework for semi-supervised medical image segmentation,

Reference 9

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Observation af1ad2d0-8185-452d-a947-702822dd0297 · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 10

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Observation 7bff9123-8c26-4776-916d-5af0f27222b3 · outbound

This paper cites Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation,

Reference 11

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Observation 13f9bb48-23db-4c58-80e1-ab3cb076fb12 · outbound

This paper cites Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmenta- tion,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmenta- tion,

Reference 12

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Observation 539e345a-a385-411e-95ec-9bf33de5188c · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning,

Reference 13

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Observation 9aa517df-6a5b-4401-ba3f-07f96252747b · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation

Reference 14

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Observation 25dd5d40-3825-4048-9aef-c61aeaa44011 · outbound

This paper cites Semi-supervised contrastive learn- ing for label-efficient medical image segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Semi-supervised contrastive learn- ing for label-efficient medical image segmentation,

Reference 15

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Observation ad86d8eb-4075-4369-9d4b-e07cdfea5402 · outbound

This paper cites Dense biased networks with deep priori anatomy and hard region adaptation: Semi- supervised learning for fine renal artery segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Dense biased networks with deep priori anatomy and hard region adaptation: Semi- supervised learning for fine renal artery segmentation,

Reference 16

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Observation 409eddfe-c337-436d-bd37-c3b2c1622cd8 · outbound

This paper cites Segment Anything.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Segment Anything

Reference 17

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Observation a6979447-7b9f-430f-adf0-76681277b8ce · outbound

This paper cites SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 18

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Observation 84bf10a6-d222-44e3-9307-910556f756fb · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 19

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Observation 2a383814-1f5d-4114-95c4-61fc67e03615 · outbound

This paper cites Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation

Reference 20

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Observation 73cbee35-6f90-4701-a6b1-a0e84c1a5b2e · outbound

This paper cites De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation

Reference 21

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Observation c03d78d8-cdf4-4814-9592-b7c649b913ea · outbound

This paper cites SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization

Reference 22

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Observation 01be1d59-c2e9-4ad9-aeb0-85caaff60391 · outbound

This paper cites Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification

Reference 23

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This paper cites SS-TBN: A semi-supervised tri-branch network for covid-19 screening and lesion segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation SS-TBN: A semi-supervised tri-branch network for covid-19 screening and lesion segmentation,

Reference 24

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Observation d48e92a1-5369-44ab-bb3c-55346ae684a5 · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Enhancing pseudo label quality for semi- supervised domain-generalized medical image segmentation,

Reference 25

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Observation 91fbfadb-45b3-4c9c-9b22-d7638dbb1aef · outbound

This paper cites Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation,

Reference 26

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Observation fea4f165-7e94-4833-b6f9-d47fff3c0a9b · outbound

This paper cites SSA-Net: Spatial self-attention network for COVID- 19 pneumonia infection segmentation with semi-supervised few-shot learning,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation SSA-Net: Spatial self-attention network for COVID- 19 pneumonia infection segmentation with semi-supervised few-shot learning,

Reference 27

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Semi-supervised neuron segmentation via reinforced consistency learn- ing,

Reference 28

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Observation bb554101-0bd8-4468-a7c9-a7bb5894e0ff · outbound

This paper cites Double noise mean teacher self- ensembling model for semi-supervised tumor segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Double noise mean teacher self- ensembling model for semi-supervised tumor segmentation,

Reference 29

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Observation 0e5222fb-8f56-4851-8156-70b4583bd52c · outbound

This paper cites Revisiting weak-to- strong consistency in semi-supervised semantic segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Revisiting weak-to- strong consistency in semi-supervised semantic segmentation,

Reference 30

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Observation 78dd8dc5-adfa-46fe-9ee6-02fb154b5277 · outbound

This paper cites MTANS: multi-scale mean teacher combined adversarial network with shape-aware embedding for semi-supervised brain lesion segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation MTANS: multi-scale mean teacher combined adversarial network with shape-aware embedding for semi-supervised brain lesion segmentation,

Reference 31

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Observation 44bd4aff-3cd2-4123-af46-fe45eb5851a3 · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Uncertainty- guided dual-views for semi-supervised volumetric medical image seg- mentation,

Reference 32

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Observation 2ea202fd-6500-4a70-aadd-1f981ac723c8 · outbound

This paper cites Collaborative and adversarial learning of focused and dispersive representations for semi-supervised polyp segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Collaborative and adversarial learning of focused and dispersive representations for semi-supervised polyp segmentation,

Reference 33

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Observation 1c6e36f3-ba4e-46f2-a6da-43ecc4ba1224 · outbound

This paper cites Deep mutual distillation for semi- supervised medical image segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Deep mutual distillation for semi- supervised medical image segmentation,

Reference 34

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Observation 3f6d913c-9351-4f2e-ba7e-31f3466fc3c0 · outbound

This paper cites Simcvd: Simple contrastive voxel-wise representation distillation for semi-supervised medical image segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Simcvd: Simple contrastive voxel-wise representation distillation for semi-supervised medical image segmentation,

Reference 35

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Observation f226b0f8-a1da-4e40-84ec-0ab86cbe2a66 · outbound

This paper cites Bootstrapping semi-supervised medical image segmentation with anatomical-aware contrastive distillation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Bootstrapping semi-supervised medical image segmentation with anatomical-aware contrastive distillation,

Reference 36

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Observation 172d50c2-3603-42f4-b88c-20f1c54f565d · outbound

This paper cites Cross-mix monitoring for medical image segmentation with limited supervision,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Cross-mix monitoring for medical image segmentation with limited supervision,

Reference 37

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Observation 377f8b49-f976-4170-bf4e-0b6a044c12df · outbound

This paper cites S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation

Reference 38

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Observation de5043da-f645-4e61-89fa-049df7556595 · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Semi-supervised semantic segmentation with cross pseudo supervision,

Reference 39

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Observation c855d41a-057a-415c-812a-e36e16981ab3 · outbound

This paper cites Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions

Reference 40

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Observation aadee302-9bc7-442e-8a9a-ab8e4938d062 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Distilling the Knowledge in a Neural Network

Reference 41

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Observation 160b64aa-9a05-47bf-9390-dac8afc84417 · outbound

This paper cites Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.740000Z

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Observation 6690e377-12c2-4127-8839-f80c1ed0ac42 · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Constructing and exploring intermediate domains in mixed domain semi-supervised medical image segmentation,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.728982Z

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Observation 648c365d-d410-4868-bc22-33402f390b29 · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Mutual consistency learning for semi-supervised medical image segmentation,

Reference 44

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Observation 11176ffb-13a0-4732-8978-e3abfcee7a07 · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Caussl: Causality- inspired semi-supervised learning for medical image segmentation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.711410Z

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Observation fbda042f-97a0-4d66-9a6e-3f52357ea1da · outbound

This paper cites Bilateral supervision network for semi-supervised medical image segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Bilateral supervision network for semi-supervised medical image segmentation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.700069Z

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Observation 2162e6c9-f3f8-48f8-ab23-b12d297953c1 · outbound

This paper cites Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer,

Reference 47

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Observation 81555d54-9c95-4a38-b86e-3c0ff7991305 · outbound

This paper cites WM-DOV A maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation WM-DOV A maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians,

Reference 48

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Observation b789c397-3632-410e-b618-42e2a41d1bff · outbound

This paper cites Automated polyp detection in colonoscopy videos using shape and context information,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Automated polyp detection in colonoscopy videos using shape and context information,

Reference 49

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Observation c8f45745-8b0d-4684-8342-4cecb080331e · outbound

This paper cites A benchmark for endoluminal scene segmentation of colonoscopy images,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation A benchmark for endoluminal scene segmentation of colonoscopy images,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.660316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e4b95afc-e5ad-4527-b802-17820adb5e8a · outbound

This paper cites Kvasir-seg: A segmented polyp dataset,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Kvasir-seg: A segmented polyp dataset,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.649927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5424d11d-47d2-4def-be47-eb3f57a89657 · outbound

This paper cites Pranet: Parallel reverse attention network for polyp segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Pranet: Parallel reverse attention network for polyp segmentation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.639627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 891f846f-424a-433b-b652-aad971d75b17 · outbound

This paper cites Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.628823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f7de666e-ae95-4bcc-913c-121fa8926ace · outbound

This paper cites Comparison of thyroid segmentation techniques for 3D ultrasound,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Comparison of thyroid segmentation techniques for 3D ultrasound,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.617738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 08b2beaa-a2ba-443e-975b-0c5d0e157022 · outbound

This paper cites An open access thyroid ultrasound image database,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation An open access thyroid ultrasound image database,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.607893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1956fe3a-e1fc-4d08-baba-6eaef3b3570b · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T12:54:14.346923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cc609c0-8f12-46ef-98a3-256502b4c7e5 · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.596735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 374e9cd6-1a4d-44c3-a3f1-14b64678e036 · outbound

This paper cites Structured crowdsourcing enables convolutional segmentation of histology images,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Structured crowdsourcing enables convolutional segmentation of histology images,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.585754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d6fd7cd7-7591-4b84-80fc-87a4e189cbde · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation U-net: Convolutional net- works for biomedical image segmentation,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.574276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1849ac68-466e-4c4b-93e8-7886c1655725 · outbound

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

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.561900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 27835fc0-7a68-4d27-93d1-10820357f0bd · outbound

This paper cites Cross prompting consistency with segment anything model for semi- supervised medical image segmentation,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Cross prompting consistency with segment anything model for semi- supervised medical image segmentation,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:14.550002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8e24426d-8194-42df-b830-bdf802c5d9b9 · outbound

This paper cites Segment anything in medical images,.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Segment anything in medical images,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T12:54:14.368120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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