Paper Citation Record · LEDGER

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2304.12620.

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

pith.paper-citation-record.v1
2304.12620 v7

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measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 31 of 31 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 31 of 31 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T10:16:59.172197Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:08:43.558543Z

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3f7831fb-869c-41f3-bc1d-9a8232d4e3da · inbound

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

SAM 2: Segment Anything in Images and Videos Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:56:25.462995Z

Source-reported events for the cited work

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

Observation df79e754-b358-4446-9f10-6f867a677fa7 · inbound

Towards Fine-grained Interactive Segmentation in Images and Videos cites this paper.

Towards Fine-grained Interactive Segmentation in Images and Videos Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T10:16:59.172197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc500675-96ef-4068-a72e-3fe1e92fd449 · inbound

COMMA: Coordinate-aware Modulated Mamba Network for 3D Dispersed Vessel Segmentation cites this paper.

COMMA: Coordinate-aware Modulated Mamba Network for 3D Dispersed Vessel Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:47:22.334957Z

Source-reported events for the cited work

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

Observation 528dd570-f869-456f-9ffc-20dc64cbf566 · inbound

TAGS: 3D Tumor-Adaptive Guidance for SAM cites this paper.

TAGS: 3D Tumor-Adaptive Guidance for SAM Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T15:28:40.023093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fa47d21-827d-415a-8d7b-019097e79d86 · inbound

SAM-I2V: Upgrading SAM to Support Promptable Video Segmentation with Less than 0.2% Training Cost cites this paper.

SAM-I2V: Upgrading SAM to Support Promptable Video Segmentation with Less than 0.2% Training Cost Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:48.476606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cb773e5-94e0-48c3-ab0e-922210878095 · inbound

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation cites this paper.

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

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:36.359237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cff38be-7265-47a7-b656-36aa375fb443 · inbound

SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation cites this paper.

SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T04:56:11.853828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e08f683-eb1a-4e79-89ba-7915a4b7d6a7 · inbound

Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation cites this paper.

Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:24.401174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcc2d600-a684-44c8-ac7f-6921269badb6 · inbound

Evolutionary computing-based image segmentation method to detect defects and features in Additive Friction Stir Deposition Process cites this paper.

Evolutionary computing-based image segmentation method to detect defects and features in Additive Friction Stir Deposition Process Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:16.251683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5316cdec-2572-4d8f-b73d-9e6de7ce5019 · inbound

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment cites this paper.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:48.265669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da19b629-5942-4572-b2df-aa2e5df9b4d2 · inbound

PromptForSegCXR: Prompt-Driven Multi-Organ and Multi-Disease Segmentation in Chest X-rays using a Multi-stage Fusion Mechanism cites this paper.

PromptForSegCXR: Prompt-Driven Multi-Organ and Multi-Disease Segmentation in Chest X-rays using a Multi-stage Fusion Mechanism Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T21:15:08.542922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7003fdcf-c75c-436d-84d4-21016e5f4134 · inbound

Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation cites this paper.

Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T20:55:07.752330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd4bfa4b-3cc3-4f63-b25a-dc6df4fad9d6 · inbound

Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation cites this paper.

Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T17:58:46.645405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e2cbf5a-6a89-45f6-acf3-00868566ffe7 · inbound

Region-aware Depth Scale Adaptation with Sparse Measurements cites this paper.

Region-aware Depth Scale Adaptation with Sparse Measurements Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T15:53:59.233259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b58253a0-7441-4875-9dcd-ded5ed17c999 · inbound

Register Anything: Estimating "Corresponding Prompts" for Segment Anything Model cites this paper.

Register Anything: Estimating "Corresponding Prompts" for Segment Anything Model Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T05:30:10.841869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4e93cea-3af0-4a94-af03-348149ee46b8 · inbound

ParticleSAM: Small Particle Segmentation for Material Quality Monitoring in Recycling Processes cites this paper.

ParticleSAM: Small Particle Segmentation for Material Quality Monitoring in Recycling Processes Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T04:28:30.949233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf8ab717-4015-494f-947c-8c314b33e629 · inbound

MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy cites this paper.

MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-06T04:24:28.200681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e11ff524-d44d-4b46-b372-17654bd44545 · inbound

Multimodal SAM-adapter for Semantic Segmentation cites this paper.

Multimodal SAM-adapter for Semantic Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T17:57:10.279609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98c77fc2-deef-4406-87df-c7a7cec3f22a · inbound

SegSLR: Promptable Video Segmentation for Isolated Sign Language Recognition cites this paper.

SegSLR: Promptable Video Segmentation for Isolated Sign Language Recognition Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:53.185249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddf2c4df-20da-49b8-b704-838914e8a694 · inbound

Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement cites this paper.

Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 80

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T18:13:13.281011Z

Source-reported events for the cited work

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

Observation 80ee9de7-bae0-4719-9aa5-2984ff2ffbfa · inbound

Learning to Synergize Semantic and Geometric Priors for Limited-Data Wheat Disease Segmentation cites this paper.

Learning to Synergize Semantic and Geometric Priors for Limited-Data Wheat Disease Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:05:51.515675Z

Source-reported events for the cited work

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

Observation 4de3b2bb-2415-4552-abe4-4b7684a8dc76 · inbound

PR-MaGIC: Prompt Refinement Via Mask Decoder Gradient Flow For In-Context Segmentation cites this paper.

PR-MaGIC: Prompt Refinement Via Mask Decoder Gradient Flow For In-Context Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:46:05.624882Z

Source-reported events for the cited work

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

Observation 4973f6ea-3ef2-490d-a79b-d281ddd82f0b · inbound

Align then Refine: Text-Guided 3D Prostate Lesion Segmentation cites this paper.

Align then Refine: Text-Guided 3D Prostate Lesion Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:05:23.473716Z

Source-reported events for the cited work

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

Observation c25849c9-984d-4a02-b663-83ee6c548294 · inbound

SAMamba3D: adapting Segment Anything for generalizable 3D segmentation of multiphase pore-scale images cites this paper.

SAMamba3D: adapting Segment Anything for generalizable 3D segmentation of multiphase pore-scale images Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:21:10.344103Z

Source-reported events for the cited work

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

Observation 1791d043-2dae-4005-a288-a6b7e7cb597f · inbound

Deep Reprogramming Distillation for Medical Foundation Models cites this paper.

Deep Reprogramming Distillation for Medical Foundation Models Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:25:38.776947Z

Source-reported events for the cited work

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

Observation e9200b79-5c57-4814-ab6f-1ddfe4473745 · inbound

Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study cites this paper.

Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:51:25.509104Z

Source-reported events for the cited work

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

Observation cbad2151-86a3-4ab3-91d0-d3329bedd87f · inbound

Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representation cites this paper.

Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T23:14:01.694934Z

Source-reported events for the cited work

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

Observation b632804d-d66c-4507-af6c-f68732aacde2 · inbound

DeCoDrift: Stabilizing Decoder Coupling in Closed-Loop Foundation Segmentation cites this paper.

DeCoDrift: Stabilizing Decoder Coupling in Closed-Loop Foundation Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T23:14:01.837598Z

Source-reported events for the cited work

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

Observation 689a8632-3259-4bfb-951c-a450b6a25280 · inbound

Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks cites this paper.

Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-01T22:36:17.211138Z

Source-reported events for the cited work

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

Observation 7df9dbdd-ca73-4091-becb-063002827486 · inbound

Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline cites this paper.

Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 87

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T20:57:23.227572Z

Source-reported events for the cited work

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

Observation de097f89-60b4-41d8-887f-86a842eadeb4 · inbound

Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images cites this paper.

Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:08:43.559845Z

Source-reported events for the cited work

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