Paper Citation Record · LEDGER

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting

As of 20 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2506.19658.

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

pith.paper-citation-record.v1
2506.19658 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:32:46.965822Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 15786fdb-930d-47a3-a784-f8ea15fd5690 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting On the Opportunities and Risks of Foundation Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.736684Z

Source-reported events for the cited work

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Observation 12e0c6e8-1985-4fa8-89f4-474fb5367533 · outbound

This paper cites Segment Anything.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Segment Anything

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.743018Z

Source-reported events for the cited work

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Observation 4cb0a3b8-1891-4ebd-953e-45996428203d · outbound

This paper cites Segment anything model (sam) for medical image segmentation: A preliminary review,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Segment anything model (sam) for medical image segmentation: A preliminary review,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:48.034276Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 18498795-8762-4bfc-98d9-8f037667ce87 · outbound

This paper cites A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.754079Z

Source-reported events for the cited work

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Observation 60065c35-3456-4878-ae4c-ba93056eda6c · outbound

This paper cites AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.759724Z

Source-reported events for the cited work

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Observation c36cda50-40f5-4a8a-ad7c-c8f5afd6c9c3 · outbound

This paper cites Segment Anything in Medical Images and Videos: Benchmark and Deployment.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Segment Anything in Medical Images and Videos: Benchmark and Deployment

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.765015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 035cc532-524a-455b-857a-1b2b5b299513 · outbound

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

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.771154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4802da42-c37e-43ee-a59f-bfdafaf36742 · outbound

This paper cites SAM3D: Segment Anything in 3D Scenes.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting SAM3D: Segment Anything in 3D Scenes

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.776641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 310b952f-f467-4612-9756-b3e76817527d · outbound

This paper cites 3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable tumor segmentation,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting 3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable tumor segmentation,

Reference 9

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Source-reported events for the cited work

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Observation 985271b3-ee52-4e3f-b208-30364206d7e8 · outbound

This paper cites Ma-sam: Modality- agnostic sam adaptation for 3d medical image segmentation,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Ma-sam: Modality- agnostic sam adaptation for 3d medical image segmentation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:48.016633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8c1396f4-6471-41c5-b65c-533c86deef10 · outbound

This paper cites MedLSAM: Localize and Segment Anything Model for 3D CT Images.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting MedLSAM: Localize and Segment Anything Model for 3D CT Images

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:32:47.532900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 47e63751-ce0b-459c-8744-bfa688882dd7 · outbound

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

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting SAM 2: Segment Anything in Images and Videos

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.795048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f429e344-cfab-405f-a769-313056c3c5d9 · outbound

This paper cites RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.799855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23b851ac-e1c9-42dc-a1ba-bf773cc624d6 · outbound

This paper cites Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.804319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 746bdbb5-b3e7-4841-ae1d-6c486f956398 · outbound

This paper cites Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.808841Z

Source-reported events for the cited work

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Observation b9587245-78f8-43e2-9860-0e6e70914b59 · outbound

This paper cites Medical sam 2: Segment medical images as video via segment anything model 2,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Medical sam 2: Segment medical images as video via segment anything model 2,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.997864Z

Source-reported events for the cited work

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Observation 066523b1-42d7-4745-b01d-491e3657a014 · outbound

This paper cites Interactive 3D Medical Image Segmentation with SAM 2.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Interactive 3D Medical Image Segmentation with SAM 2

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.824746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50f0eec7-e6b0-406b-8fc7-56f2c980df6b · outbound

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

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Biomedical SAM 2: Segment Anything in Biomedical Images and Videos

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.830288Z

Source-reported events for the cited work

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Observation 75b450cf-e5fe-4856-bf0b-996d69c3dfe3 · outbound

This paper cites A Short Review and Evaluation of SAM2's Performance in 3D CT Image Segmentation.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting A Short Review and Evaluation of SAM2's Performance in 3D CT Image Segmentation

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:32:47.390282Z

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Observation 17713615-79ba-40aa-98e4-ef9e5f5b8ab9 · outbound

This paper cites Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.840494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c45a86c9-1f8a-4365-9424-299f6f732804 · outbound

This paper cites Multi-atlas abdomen labeling challenge,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Multi-atlas abdomen labeling challenge,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.980876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ba20ac22-a6c3-4d16-9550-bcc26a8000c5 · outbound

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

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting DINOv2: Learning Robust Visual Features without Supervision

Reference 22

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Source-reported events for the cited work

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Observation fa60b14e-1e9b-4941-9c96-30fb02614d41 · outbound

This paper cites Vrp-sam: Sam with visual reference prompt,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Vrp-sam: Sam with visual reference prompt,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.963492Z

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Observation 7c1f4e73-da2e-400d-af7a-6bb47a514ce2 · outbound

This paper cites Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation

Reference 24

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Observation 95ce35d6-22b9-4694-8bb2-3d79653e2db5 · outbound

This paper cites Available: https://arxiv.org/abs/2402.17726.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Available: https://arxiv.org/abs/2402.17726

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.860516Z

Source-reported events for the cited work

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Observation d26cdcd9-1db5-47c1-9126-3b89250011a6 · outbound

This paper cites Icl-sam: Synergizing in-context learning model and sam in medical image segmentation,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Icl-sam: Synergizing in-context learning model and sam in medical image segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.927052Z

Source-reported events for the cited work

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Observation 1be94a8d-e536-478c-965d-f744c7bb4826 · outbound

This paper cites Universeg: Universal medical image segmentation,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Universeg: Universal medical image segmentation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.945678Z

Source-reported events for the cited work

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Observation 606988ab-ae5b-4f51-8650-29fb397bc4b1 · outbound

This paper cites Mask3d: Mask transformer for 3d semantic instance segmentation,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Mask3d: Mask transformer for 3d semantic instance segmentation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.898248Z

Source-reported events for the cited work

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Observation 00b8f4a5-6683-4cc6-8bc1-7b57e69e8c30 · outbound

This paper cites Masked-attention mask transformer for universal image segmenta- tion,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Masked-attention mask transformer for universal image segmenta- tion,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.881851Z

Source-reported events for the cited work

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Observation eb9ef492-eabf-46c2-a41d-24a5c6e30f59 · outbound

This paper cites Lora: Low-rank adaptation of large language models,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Lora: Low-rank adaptation of large language models,

Reference 30

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Source-reported events for the cited work

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Observation f2cdad99-961b-441b-af55-223bb343afd8 · outbound

This paper cites A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.912006Z

Source-reported events for the cited work

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Observation 9f23e354-d74a-4040-8424-9b497000ed87 · outbound

This paper cites Automatic segmentation of mandible in panoramic x-ray,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Automatic segmentation of mandible in panoramic x-ray,

Reference 32

Resolution: verified exact; doi, observed 2026-08-15T18:32:47.017750Z

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Observation b84522a4-8578-490e-9549-acde83047b84 · outbound

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

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Deep learning for segmentation using an open large-scale dataset in 2d echocardiography,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.921914Z

Source-reported events for the cited work

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Observation 11ddf2bf-9cb3-4476-8b01-9ce439d23929 · outbound

This paper cites Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:32:47.219190Z

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Observation e465ad33-a5e5-401d-abbb-1dd318ac7a77 · outbound

This paper cites Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.848950Z

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Observation 7dbbdd84-5cad-40ca-8e19-77ca3fc26265 · outbound

This paper cites Swincross: Cross- modal swin transformer for head-and-neck tumor segmentation in pet/ct images,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Swincross: Cross- modal swin transformer for head-and-neck tumor segmentation in pet/ct images,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.828544Z

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Observation e9dbb1ca-037c-4c03-8e43-6027f8a202e5 · outbound

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

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved?

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.810604Z

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Observation c806916b-f5c1-44d6-ac13-1eb564690a01 · outbound

This paper cites Dataset of breast ultrasound images,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Dataset of breast ultrasound images,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.866086Z

Source-reported events for the cited work

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Observation 54db7bfa-76c5-4976-b3b1-37daa509fa43 · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Unetr: Transformers for 3d medical image segmentation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.774633Z

Source-reported events for the cited work

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Observation 7a9f4fe8-aa3d-46e2-b811-fba94c65a72d · outbound

This paper cites Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.956281Z

Source-reported events for the cited work

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Observation 27be8cfa-c8f0-4345-b493-778568374ab8 · outbound

This paper cites nnu-net: a self-configuring method for deep learning- based biomedical image segmentation,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting nnu-net: a self-configuring method for deep learning- based biomedical image segmentation,

Reference 41

Resolution: malformed identifier; no resolver link, observed 2026-08-15T18:32:46.961059Z

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Observation 42a30e9a-05d2-4323-b632-977b6f736f54 · outbound

This paper cites AMOS: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting AMOS: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.792195Z

Source-reported events for the cited work

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Observation 1ab4d44f-c986-4211-9c20-da78ff216504 · outbound

This paper cites Pet image denoising based on 3d denoising diffusion probabilistic model: Evaluations on total-body datasets,.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Pet image denoising based on 3d denoising diffusion probabilistic model: Evaluations on total-body datasets,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:47.757366Z

Source-reported events for the cited work

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Observation d1ecbd3e-bf2b-4cab-805f-1582df7eb104 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting LoRA: Low-Rank Adaptation of Large Language Models

Reference 2021

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Source-reported events for the cited work

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Observation 056e1f24-e60f-4c0d-9db8-a0ca664ee8f4 · outbound

This paper cites UniverSeg: Universal Medical Image Segmentation.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting UniverSeg: Universal Medical Image Segmentation

Reference 2023

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Source-reported events for the cited work

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Observation 54688f59-303f-43c3-a99a-e97cf6625ae5 · outbound

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

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Medical SAM 2: Segment medical images as video via Segment Anything Model 2

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.819355Z

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Pith citing papers

No inbound Pith citation observations are available.