Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain | Paper Citation Record

Observation 0d522537-8d99-404d-8ea4-48d2495c7baa · outbound

This paper cites Role of segmentation in medical imaging: A compara- tive study.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Role of segmentation in medical imaging: A compara- tive study

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.462165Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ad669645-7da0-4357-8e0d-0410147a85ff · outbound

This paper cites ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.731123Z

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Observation 8aea59ae-7ab0-469d-8634-4c85b67292dd · outbound

This paper cites V oxelmorph: a learning framework for deformable medical image registration.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain V oxelmorph: a learning framework for deformable medical image registration

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.444602Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0fb26121-0bae-45c2-bee6-f057d3f7136c · outbound

This paper cites Visual prompting via image inpaint- ing.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Visual prompting via image inpaint- ing

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.428039Z

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Observation a2472ff9-26ce-41a6-89f1-6f54d7affeb2 · outbound

This paper cites an unresolved cited work.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:37:04.406187Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 572b2958-1975-42e0-b676-9eb56999a36d · outbound

This paper cites Lan- guage models are few-shot learners.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Lan- guage models are few-shot learners

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.750732Z

Source-reported events for the cited work

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Observation 9bc4927b-b503-4944-8cc5-46d4178baa70 · outbound

This paper cites Lan- guage models are few-shot learners.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Lan- guage models are few-shot learners

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.756421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b382b340-0762-4e65-ae18-a89a1f71a9f4 · outbound

This paper cites UniverSeg: Universal Medical Image Segmentation.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain UniverSeg: Universal Medical Image Segmentation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.760984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f47b7e44-8dcc-48aa-9298-53d83171760c · outbound

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

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Semi-supervised task-driven data augmentation for medical image segmentation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.365821Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 47ac2de2-74ae-4754-ac7f-73d3233397bb · outbound

This paper cites Remedios, Shunxing Bao, Bennett A.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Remedios, Shunxing Bao, Bennett A

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.348902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c34da6c9-19d4-4b6b-bc94-4cad7f0dae36 · outbound

This paper cites Measures of the amount of ecologic association between species.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Measures of the amount of ecologic association between species

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.777728Z

Source-reported events for the cited work

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Observation 58e42223-5f80-4c93-ae21-9c6993cc6235 · outbound

This paper cites A Survey on In-context Learning.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain A Survey on In-context Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.781757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30402e6f-0d40-4586-b56d-cc846b4d6fb8 · outbound

This paper cites Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.314394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6eeb347f-fd29-4363-9733-5a65e6dfb1a5 · outbound

This paper cites SimCSE: Simple Contrastive Learning of Sentence Embeddings.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain SimCSE: Simple Contrastive Learning of Sentence Embeddings

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.792578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41a30263-f5ad-4022-a1db-aab5237ebcb5 · outbound

This paper cites Variational encoding and decoding for hybrid supervision of registration network.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Variational encoding and decoding for hybrid supervision of registration network

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.293430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6bc8b139-3aaf-4e39-92f2-10038bfcbdae · outbound

This paper cites Domain adaptation for medical image analysis: A survey.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Domain adaptation for medical image analysis: A survey

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.276550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7a44ea71-1f26-495c-b93a-1b87bc6a5777 · outbound

This paper cites Ellen Grant, and Yangming Ou.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Ellen Grant, and Yangming Ou

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.260545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 827e6547-60a6-4294-ab2c-de9aa2c8d13e · outbound

This paper cites Learn2reg: comprehensive multi-task medical image regis- tration challenge, dataset and evaluation in the era of deep learning.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Learn2reg: comprehensive multi-task medical image regis- tration challenge, dataset and evaluation in the era of deep learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.246155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 85b62b25-0e86-494b-938b-9d32afec6b46 · outbound

This paper cites When sam meets medical images: An investigation of seg- ment anything model (sam) on multi-phase liver tumor seg- mentation, 2023.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain When sam meets medical images: An investigation of seg- ment anything model (sam) on multi-phase liver tumor seg- mentation, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.226563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b1b93888-7b8f-459b-bfe9-bf947c1d33ac · outbound

This paper cites Many-to-many splatting for efficient video frame interpola- tion.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Many-to-many splatting for efficient video frame interpola- tion

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.826793Z

Source-reported events for the cited work

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Observation a1d35474-40c3-450f-9ec2-40bb03854a95 · outbound

This paper cites Two public chest x-ray datasets for computer-aided screening of pulmonary diseases.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Two public chest x-ray datasets for computer-aided screening of pulmonary diseases

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.200465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation abe0830c-2d7d-4d83-82c9-c56b94fa0700 · outbound

This paper cites Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-12T13:37:03.492957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0650a637-9a4e-4541-b9b9-795fe93794ac · outbound

This paper cites On the effect of inter-observer variability for a re- liable estimation of uncertainty of medical image segmenta- tion.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain On the effect of inter-observer variability for a re- liable estimation of uncertainty of medical image segmenta- tion

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.174766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e83806e0-9911-40db-bf66-56522a38e135 · outbound

This paper cites Harmony4D: A Video Dataset for In-The-Wild Close Human Interactions.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Harmony4D: A Video Dataset for In-The-Wild Close Human Interactions

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-12T13:37:03.472945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ba2d5d90-d892-4f32-8855-0d5a6da843f5 · outbound

This paper cites Data-efficient unsupervised interpolation without any intermediate frame for 4d medical images.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Data-efficient unsupervised interpolation without any intermediate frame for 4d medical images

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.156781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c89f4a8c-acf6-4c53-b2a1-9505e6e3df25 · outbound

This paper cites Segment Anything.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Segment Anything

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.871856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44e74f97-c4fc-49d9-87d0-54e2d2d26a35 · outbound

This paper cites Buu-lspine: A thai open lumbar spine dataset for spondylolisthesis detection.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Buu-lspine: A thai open lumbar spine dataset for spondylolisthesis detection

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.140532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 69e3c75f-08ae-474b-ba4b-f3b852e1fbe3 · outbound

This paper cites New index for cluster- ing tendency and its application to chemical problems.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain New index for cluster- ing tendency and its application to chemical problems

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.120905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 377dffac-bfe9-4c66-8580-2502a3c43b1b · outbound

This paper cites Deep learning for segmentation using an open large-scale dataset in 2d echocardiography.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Deep learning for segmentation using an open large-scale dataset in 2d echocardiography

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.101378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ec5d69f2-c1c9-4403-be31-49853750e33f · outbound

This paper cites Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.894792Z

Source-reported events for the cited work

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Observation cca99586-e99c-4f32-b840-b9004816f204 · outbound

This paper cites Decoupled Weight Decay Regularization.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Decoupled Weight Decay Regularization

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.899401Z

Source-reported events for the cited work

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Observation 78fab903-1263-4fca-86fe-f8b9fd2cfc68 · outbound

This paper cites Segment anything in medical images.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Segment anything in medical images

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.904231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab786ff1-2cdc-4af8-888a-2a72984b1ea0 · outbound

This paper cites Learn- ing deformable registration of medical images with anatom- ical constraints.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Learn- ing deformable registration of medical images with anatom- ical constraints

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.069006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ea2598ac-a003-44b1-9106-fc7a831cd362 · outbound

This paper cites Non-iterative coarse-to-fine transformer net- works for joint affine and deformable image registration.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Non-iterative coarse-to-fine transformer net- works for joint affine and deformable image registration

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.050625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6407b2e0-7a1c-4126-9ce4-b0fd03518521 · outbound

This paper cites Correlation-aware coarse-to-fine mlps for deformable medi- cal image registration.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Correlation-aware coarse-to-fine mlps for deformable medi- cal image registration

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.034682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fb83e7a2-1062-4e70-9999-30d09674ebd0 · outbound

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

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:04.011892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 994d43df-79fc-4eab-8b81-b9e6e371069d · outbound

This paper cites Fast binary dilation/erosion algorithm us- ing kernel subdivision.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Fast binary dilation/erosion algorithm us- ing kernel subdivision

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.986651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d675d8b3-ffaa-441a-87e0-49070a17305b · outbound

This paper cites Context-aware synthesis for video frame interpolation.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Context-aware synthesis for video frame interpolation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.967063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e644e015-ff36-4ddc-9862-8981d5ec6e0c · outbound

This paper cites GPT-4 Technical Report.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain GPT-4 Technical Report

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.942720Z

Source-reported events for the cited work

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Observation 76ac5ec5-00f1-4242-93c4-d48490a03561 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain DINOv2: Learning Robust Visual Features without Supervision

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.955256Z

Source-reported events for the cited work

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Observation afcdf0ad-2d85-4196-93a2-3efecabaa903 · outbound

This paper cites Video-based ai for beat-to-beat assessment of cardiac func- tion.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Video-based ai for beat-to-beat assessment of cardiac func- tion

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.960224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea7b2908-6c5e-489c-985d-4ee449f6064a · outbound

This paper cites Limitations of the ssim quality metric in the context of diagnostic imaging.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Limitations of the ssim quality metric in the context of diagnostic imaging

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.902052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d93846dc-7b23-4f64-a3b2-2c51f03a411c · outbound

This paper cites Asymmetric bilateral motion estimation for video frame interpolation.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Asymmetric bilateral motion estimation for video frame interpolation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.883720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c389c4c6-2daf-407e-90c8-e01fe3e76061 · outbound

This paper cites Biformer: Learning bilateral motion estimation via bilateral trans- former for 4k video frame interpolation.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Biformer: Learning bilateral motion estimation via bilateral trans- former for 4k video frame interpolation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.858453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 07e91582-c764-4bb3-9131-d76eca7c883d · outbound

This paper cites Can we trust deep learning based diagnosis? the impact of domain shift in chest radiograph classification.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Can we trust deep learning based diagnosis? the impact of domain shift in chest radiograph classification

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.835529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 51ede80f-073a-459c-b31f-a6278479827e · outbound

This paper cites Improving language understanding by gen- erative pre-training.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Improving language understanding by gen- erative pre-training

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.987957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8be75318-aba9-40ed-b3e6-a5adc49bc5c3 · outbound

This paper cites Language models are unsu- pervised multitask learners.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Language models are unsu- pervised multitask learners

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.991852Z

Source-reported events for the cited work

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Observation 498df00e-15cd-4268-ab34-16cb82173016 · outbound

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

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain SAM 2: Segment Anything in Images and Videos

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.996004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b79fbbbf-786b-47d6-97dc-97e5c7e81792 · outbound

This paper cites Contrastive Learning with Hard Negative Samples.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Contrastive Learning with Hard Negative Samples

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:03.002185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd9d80a0-f2f5-49e5-be2c-e0a07fe737fd · outbound

This paper cites Is SAM 2 Better than SAM in Medical Image Segmentation?.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Is SAM 2 Better than SAM in Medical Image Segmentation?

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:03.008531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b57a2f8f-d5db-4933-a907-28c359881f09 · outbound

This paper cites an unresolved cited work.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:37:03.789437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 85889404-d324-4358-8448-685cf28e8e67 · outbound

This paper cites Medical image registration based on uncoupled learning and accumulative enhancement.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Medical image registration based on uncoupled learning and accumulative enhancement

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.771089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a0e9a9b8-1163-43bd-9218-b1d87f89d766 · outbound

This paper cites OdontoAI: A human-in-the-loop labeled data set and an online platform to boost research on dental panoramic radiographs.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain OdontoAI: A human-in-the-loop labeled data set and an online platform to boost research on dental panoramic radiographs

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:03.027486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bab0e869-efe8-400f-bee6-f73ffe8e3138 · outbound

This paper cites ⊥-loss: A symmetric loss function for magnetic resonance imaging reconstruction and image registration with deep learning.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain ⊥-loss: A symmetric loss function for magnetic resonance imaging reconstruction and image registration with deep learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.754115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 303cd4c8-b554-4c3c-93bb-2168d7f930ed · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain LLaMA: Open and Efficient Foundation Language Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:03.037261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec3c7134-521d-4773-9356-34275a6331b7 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:03.042700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aab722e-f659-4bc6-8702-46f1210c5b74 · outbound

This paper cites Multi-stage transfer learning for lung segmentation using portable x-ray devices for patients with covid-19.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Multi-stage transfer learning for lung segmentation using portable x-ray devices for patients with covid-19

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.736305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 53ffe957-ec84-4056-8b27-8e7d0c33b0a8 · outbound

This paper cites Images speak in images: A generalist painter for in-context visual learning.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Images speak in images: A generalist painter for in-context visual learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.709378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0d43a893-ffca-46bb-ac83-80014f23ed16 · outbound

This paper cites SegGPT: Segmenting Everything In Context.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain SegGPT: Segmenting Everything In Context

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:03.056995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53d697a8-05d1-4777-895a-9061e543043a · outbound

This paper cites Emergent Abilities of Large Language Models.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Emergent Abilities of Large Language Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:03.068121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c74ea57a-6438-466f-b234-a39b209248e7 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large lan- guage models.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Chain-of-thought prompting elicits reasoning in large lan- guage models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.691179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f203d726-d1bb-4c1b-88b9-dd21b9cecc59 · outbound

This paper cites Prompting segment anything model with domain-adaptive prototype for generalizable medical image segmentation.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Prompting segment anything model with domain-adaptive prototype for generalizable medical image segmentation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.674737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 91ba9c29-b7e3-4184-8992-84ec61bd4f9f · outbound

This paper cites Wong, Marianne Rakic, John Guttag, and Adrian V.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Wong, Marianne Rakic, John Guttag, and Adrian V

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.661433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f0ccf7f6-a43d-4063-86e7-858a4488adb5 · outbound

This paper cites Medical sam adapter: Adapting seg- ment anything model for medical image segmentation, 2023.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Medical sam adapter: Adapting seg- ment anything model for medical image segmentation, 2023

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:03.089668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d087ac6-c340-4cab-a64b-7e53816008fc · outbound

This paper cites CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything Model.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything Model

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:03.094167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b0766ec-8b8f-49c8-b403-7f713af72de4 · outbound

This paper cites Customized Segment Anything Model for Medical Image Segmentation.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Customized Segment Anything Model for Medical Image Segmentation

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:03.099147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25e95e48-be4a-424d-b887-31c28efea0d8 · outbound

This paper cites Personalize Segment Anything Model with One Shot.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Personalize Segment Anything Model with One Shot

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:03.103901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 033235cd-4951-4c8d-b6a9-2e2b1ed95e66 · outbound

This paper cites Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:03.109251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a7c83be-0812-436c-a146-ab7c0c97e384 · outbound

This paper cites Semi-supervised cardiac image segmentation via label prop- agation and style transfer.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Semi-supervised cardiac image segmentation via label prop- agation and style transfer

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.639813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e7f1c599-56bd-41f4-b680-e1a483fa4a71 · outbound

This paper cites Can sam segment polyps?, 2023.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Can sam segment polyps?, 2023

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.626705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 19b1a31d-3bf8-42f6-9d71-5db843fe7433 · outbound

This paper cites Test-time training for deformable multi-scale image registration.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Test-time training for deformable multi-scale image registration

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.613745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 66a4788c-1856-4428-ba08-356fe6c97dbb · outbound

This paper cites Segment everything everywhere all at once.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Segment everything everywhere all at once

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.600518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 34d8b9a5-ff30-4226-98aa-0754170aee50 · outbound

This paper cites target-semantic prompting.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain target-semantic prompting

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:03.586280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers