Paper Citation Record · LEDGER

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation

As of 7 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2506.08949.

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

pith.paper-citation-record.v1
2506.08949 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:03:38.658134Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T05:37:27.060846Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T05:41:02.365277Z

Reference resolution

58 of 58 outbound references displayed

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External citation measurements

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Outbound references

Observation b681623c-052b-414a-a913-d16894d13b68 · outbound

This paper cites In: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition

Reference 1

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SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Unresolved cited work

Reference 2

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Observation a163a5db-d896-414b-8e41-57876d9f867d · outbound

This paper cites Medical Artificial Intelligence for Early Detection of Lung Cancer: A Survey.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Medical Artificial Intelligence for Early Detection of Lung Cancer: A Survey

Reference 3

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Observation 701d630d-61c3-4a11-9c51-4dff53a42f0b · outbound

This paper cites MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule

Reference 4

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Observation 3b59d36d-e1b4-4a75-bf01-19ff530ad9fc · outbound

This paper cites Medical Image Analysis98, 103310 (2024).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Medical Image Analysis98, 103310 (2024)

Reference 5

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Observation c68cd465-3c4a-42db-a370-afa4f6f1de31 · outbound

This paper cites In: International conference on medical image com- puting and computer-assisted intervention.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: International conference on medical image com- puting and computer-assisted intervention

Reference 6

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Observation 89541d92-026f-41f6-bf90-78faf87f181b · outbound

This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 7

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This paper cites In: Proceedings SSS: Semi-Supervised SAM-2 17 of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Proceedings SSS: Semi-Supervised SAM-2 17 of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 8

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This paper cites AerOSeg: Harnessing SAM for Open-Vocabulary Segmentation in Remote Sensing Images.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation AerOSeg: Harnessing SAM for Open-Vocabulary Segmentation in Remote Sensing Images

Reference 9

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Observation f5627fbf-408e-4be4-b0a9-36aaf6f54653 · outbound

This paper cites In: European Conference on Computer Vision.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: European Conference on Computer Vision

Reference 10

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Observation 7cb7746f-9ec0-4cb9-8d8b-e2c4ac40b900 · outbound

This paper cites ESA: Annotation-Efficient Active Learning for Semantic Segmentation.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation ESA: Annotation-Efficient Active Learning for Semantic Segmentation

Reference 11

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SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Unresolved cited work

Reference 12

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Observation 2ed9bae7-d2e0-4ad6-bf80-4f63f2f3da74 · outbound

This paper cites In: MIC- CAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Seg- mentation, pp.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: MIC- CAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Seg- mentation, pp

Reference 13

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Observation db3416e8-914b-42db-8193-d271b28fb518 · outbound

This paper cites arXiv e-prints pp.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation arXiv e-prints pp

Reference 14

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Observation b2d66eea-079a-46d4-bffb-057fec5da9c5 · outbound

This paper cites Computers in Biology and Medicine169, 107840 (2024).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Computers in Biology and Medicine169, 107840 (2024)

Reference 15

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Observation 7bd5dffe-3b4c-47ae-96b1-c65425342fae · outbound

This paper cites In: Proceedings of the IEEE/CVF international conference on computer vision.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Proceedings of the IEEE/CVF international conference on computer vision

Reference 16

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Observation 52af3789-fe8e-4025-b59f-98ef37429bec · outbound

This paper cites Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation

Reference 17

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Observation ece45cbb-ed05-45e7-8a18-2e378355abbc · outbound

This paper cites In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23

Reference 18

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Observation a3a41eca-82fb-4bfa-87e0-b44b7ce4ec31 · outbound

This paper cites Medical image analysis42, 60–88 (2017).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Medical image analysis42, 60–88 (2017)

Reference 19

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This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 20

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Observation 11c91aa4-ad59-4562-91cb-bfdad2b17594 · outbound

This paper cites Measurement p.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Measurement p

Reference 21

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Observation 89f7ba44-d7a7-4bb5-a45d-f6ea33149894 · outbound

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SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Unresolved cited work

Reference 22

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Observation eb84c1fe-78a9-47a9-a934-81d04da7b800 · outbound

This paper cites In: Proceedings of the IEEE/CVF international conference on computer vision.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Proceedings of the IEEE/CVF international conference on computer vision

Reference 23

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Observation a8bf8499-ef38-42de-9477-9f05a2677184 · outbound

This paper cites Nature Machine Intelligence5(7), 724–738 (2023).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Nature Machine Intelligence5(7), 724–738 (2023)

Reference 24

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Observation df5bd9ae-dbbb-43da-823b-81f7ddf4bb30 · outbound

This paper cites MediAug: Exploring Visual Augmentation in Medical Imaging.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation MediAug: Exploring Visual Augmentation in Medical Imaging

Reference 25

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Observation 28417469-e149-46dd-80e2-716a4b2424a7 · outbound

This paper cites ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer

Reference 26

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Observation 7d24c326-c83a-4d8b-8c39-9e26e1ea20d8 · outbound

This paper cites MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction

Reference 27

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Observation 4ca4bf8e-2e95-4af9-8e36-0bed8d92319f · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation SAM 2: Segment Anything in Images and Videos

Reference 28

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This paper cites In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, Oc- tober 5-9, 2015, proceedings, part III 18.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, Oc- tober 5-9, 2015, proceedings, part III 18

Reference 29

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This paper cites Advances in neural information processing systems33, 596–608 (2020).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Advances in neural information processing systems33, 596–608 (2020)

Reference 30

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This paper cites In: International Conference on Med- ical Image Computing and Computer-Assisted Intervention.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: International Conference on Med- ical Image Computing and Computer-Assisted Intervention

Reference 31

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Observation 72bdedbd-7a1b-497b-982d-207959a52b16 · outbound

This paper cites SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

Reference 32

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Observation c8284865-778a-4393-a0c5-91d0ccfb0528 · outbound

This paper cites SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation

Reference 33

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Observation 54149af4-1b71-4c06-90e2-ffd1ab02b7a3 · outbound

This paper cites Advances in neural information processing systems30(2017).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Advances in neural information processing systems30(2017)

Reference 34

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This paper cites Advances in Neural Information Processing Systems36, 8815–8827 (2023).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Advances in Neural Information Processing Systems36, 8815–8827 (2023)

Reference 35

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This paper cites In: Interna- tional Workshop on Machine Learning in Medical Imaging.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Interna- tional Workshop on Machine Learning in Medical Imaging

Reference 36

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Observation d9316bdd-9f34-40cf-bfcb-7bde5227288a · outbound

This paper cites MMCLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Training.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation MMCLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Training

Reference 37

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

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 38

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This paper cites Medical image analysis102, 103547 (2025).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Medical image analysis102, 103547 (2025)

Reference 39

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This paper cites In: International conference on medical image computing and computer-assisted intervention.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: International conference on medical image computing and computer-assisted intervention

Reference 40

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Reference 41

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Observation cdc90a2b-be41-4955-8b63-9ccf29afffd5 · outbound

This paper cites In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

Reference 42

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This paper cites In: Proceedings of the AAAI Conference on Artificial Intel- ligence.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Proceedings of the AAAI Conference on Artificial Intel- ligence

Reference 43

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This paper cites IEEE Transactions on Pattern Analysis and Machine In- telligence (2025).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation IEEE Transactions on Pattern Analysis and Machine In- telligence (2025)

Reference 44

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This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:41.722030Z

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Observation dc65e7c0-ccea-46a0-b602-82a8332fe21e · outbound

This paper cites GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation

Reference 46

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This paper cites In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:41.528180Z

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Observation eb8a2ec3-bde9-4e2e-b855-a71c916ebba4 · outbound

This paper cites an unresolved cited work.

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Reference 48

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Observation e4eccaa3-d626-4412-a677-5d7077f49312 · outbound

This paper cites In: Annual Conference on Medical Image Understanding and Analysis.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Annual Conference on Medical Image Understanding and Analysis

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:41.160372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fa48e5a9-5a33-4661-b0b2-1e3fd0ed073b · outbound

This paper cites In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI)

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:41.004246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 31e8382f-00d7-4e92-9839-f528bec1b296 · outbound

This paper cites In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:40.845699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2c4a0cf3-6ea9-4007-a31f-905c7e6075ac · outbound

This paper cites OpenReview (2023).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation OpenReview (2023)

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:40.663212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b0f160ba-2d98-4125-a5e8-6f3add6478a1 · outbound

This paper cites PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian Detection.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian Detection

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:38.042509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a05c6b7d-53c3-4b30-bce1-e8e42acf05a3 · outbound

This paper cites In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI).

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:40.457815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3880bcfb-a054-45b7-bd15-42f336a6caa5 · outbound

This paper cites In: European Conference on Computer Vision.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: European Conference on Computer Vision

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:40.249544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 135af701-9f40-4f23-b0b2-2ab8d65c68ab · outbound

This paper cites In: Proceedings of the 31st ACM International Conference on Multimedia.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation In: Proceedings of the 31st ACM International Conference on Multimedia

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:40.027626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a3691214-697f-45bc-a063-3518cd221ffd · outbound

This paper cites DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Maps.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Maps

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:38.522428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4455d35e-f1b9-413a-a42e-4be7e47c63f0 · outbound

This paper cites Medical SAM 2: Segment medical images as video via Segment Anything Model 2.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Medical SAM 2: Segment medical images as video via Segment Anything Model 2

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:38.658134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 4dc53c99-01c0-4c18-bf2b-fa9bb572af9f · inbound

SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation cites this paper.

SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:41:02.366791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.