Paper Citation Record · LEDGER

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2

As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2502.02741.

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

pith.paper-citation-record.v1
2502.02741 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:21:45.855093Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

35 of 35 outbound references displayed

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verified fuzzy16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9adb8d88-9c83-4631-bc2a-88e2a389bcbc · outbound

This paper cites SAM3D: Segment Anything Model in Volumetric Medical Images.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 SAM3D: Segment Anything Model in Volumetric Medical Images

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:21:46.024808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ece624ea-ccf6-42a4-910b-8e0bd0756448 · outbound

This paper cites Sam3d: Segment anything model in volumetric medical images.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Sam3d: Segment anything model in volumetric medical images

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.730539Z

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Observation 8b3adf5d-6049-4d4e-b91d-d1a35fceac09 · outbound

This paper cites Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.734472Z

Source-reported events for the cited work

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Observation 2ff8a161-48ad-4695-a26e-633b95132567 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.738694Z

Source-reported events for the cited work

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Observation 99219e65-d483-4eb9-beae-8adaea91eeee · outbound

This paper cites SAM-Med2D.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 SAM-Med2D

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.743002Z

Source-reported events for the cited work

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Observation 08ac3a6e-96d6-460a-b636-4922c2469f2c · outbound

This paper cites Sam-u: Multi-box prompts triggered uncertainty estimation for reliable sam in medical image.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Sam-u: Multi-box prompts triggered uncertainty estimation for reliable sam in medical image

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.247466Z

Source-reported events for the cited work

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Observation affd9d47-d942-4e16-b97c-dd72ee196b87 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 7

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Observation 90c39d31-f689-41ca-95ab-4fe3452deb90 · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.235815Z

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Observation 040edfca-79da-4fcf-949e-26f6c5c882be · outbound

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

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Unetr: Transformers for 3d medical image segmentation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.224769Z

Source-reported events for the cited work

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Observation 1cfd1716-67ce-4028-9017-50af36bc97f7 · outbound

This paper cites Masked autoencoders are scalable vision learners.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Masked autoencoders are scalable vision learners

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.213258Z

Source-reported events for the cited work

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Observation 8498b51a-aad2-4115-8144-f67503f85e09 · outbound

This paper cites Automated Design of Deep Learning Methods for Biomedical Image Segmentation.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.766112Z

Source-reported events for the cited work

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Observation c94ccdb4-2a99-4ffb-a454-baaf511f8799 · outbound

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

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Amos: A large-scale abdominal multi- organ benchmark for versatile medical image segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.202364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7e1f22c1-9133-4109-a8d8-fd967001d6e9 · outbound

This paper cites Segment Anything.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Segment Anything

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.773828Z

Source-reported events for the cited work

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Observation f1bd9ea7-9c6c-4353-b68f-7f530744636f · outbound

This paper cites Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.190089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1924287b-731b-4c6a-a228-8d7a34649d7d · outbound

This paper cites 3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.781459Z

Source-reported events for the cited work

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Observation 6d033d68-7d86-4961-a741-39bb3554cf9a · outbound

This paper cites Segment anything in medical images.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Segment anything in medical images

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.177424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 17af6b40-d11b-40aa-ae94-d39be9d6cb3f · outbound

This paper cites GPT-4 Technical Report.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 GPT-4 Technical Report

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.789356Z

Source-reported events for the cited work

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Observation 78fe36ea-4b0a-4f06-8ed0-c033883c13f8 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Learning transferable visual models from natural language supervi- sion

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.793107Z

Source-reported events for the cited work

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Observation 2f913e45-f917-489f-94c2-70400b3764e0 · outbound

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

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 SAM 2: Segment Anything in Images and Videos

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.796489Z

Source-reported events for the cited work

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Observation 465aadfa-093d-41fa-be7c-1174d537d0bf · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 U- net: Convolutional networks for biomedical image segmen- tation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.159692Z

Source-reported events for the cited work

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Observation 6dd253f7-c944-4a18-872d-94a3803430b2 · outbound

This paper cites Hi- era: A hierarchical vision transformer without the bells-and- whistles.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Hi- era: A hierarchical vision transformer without the bells-and- whistles

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.148777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 03d4ee6d-010c-44ed-a7ff-5cb80c1e5578 · outbound

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

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.807219Z

Source-reported events for the cited work

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Observation d8607281-b1de-41e6-8be0-49fc8c7564e1 · outbound

This paper cites Transbts: Multimodal brain tumor seg- mentation using transformer.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Transbts: Multimodal brain tumor seg- mentation using transformer

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.138450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3ba66913-02f2-430c-adff-be8f5550b823 · outbound

This paper cites MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.814542Z

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Observation 12e20127-b221-4895-b39f-a8e248a8222d · outbound

This paper cites Self-prompt sam: Medical image segmentation via auto- matic prompt sam adaptation, 2025.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Self-prompt sam: Medical image segmentation via auto- matic prompt sam adaptation, 2025

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.127682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e242501f-d01d-4845-a156-f88c729e4eeb · outbound

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

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Customized Segment Anything Model for Medical Image Segmentation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.821707Z

Source-reported events for the cited work

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Observation 71440a6e-dbb9-4422-a122-6fa7e59dec17 · outbound

This paper cites Segment any- thing model for medical image segmentation: Current ap- plications and future directions.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Segment any- thing model for medical image segmentation: Current ap- plications and future directions

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.117297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c538f3b-c3de-45a9-87ef-591923d0f3fe · outbound

This paper cites nnFormer: Interleaved Transformer for Volumetric Segmentation.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 nnFormer: Interleaved Transformer for Volumetric Segmentation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.828773Z

Source-reported events for the cited work

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Observation fdeddb90-ab31-4e94-b385-21268cac0f44 · outbound

This paper cites an unresolved cited work.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:21:46.106243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2d645f79-b561-4bae-9a4a-c90efab667af · outbound

This paper cites an unresolved cited work.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:21:46.095605Z

Source-reported events for the cited work

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Observation d5c628c8-7ccd-4c1d-88b3-a2211bf0952a · outbound

This paper cites Since the image encoder, the memory attention, and the mask decoder contain attention blocks for image embed- ding, which includes significant spatial information.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Since the image encoder, the memory attention, and the mask decoder contain attention blocks for image embed- ding, which includes significant spatial information

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.084012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d3d6d217-0c85-48a6-8723-f2a25b46edd1 · outbound

This paper cites an unresolved cited work.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:21:46.073029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f31b5a91-c38f-478b-a044-2896637419e2 · outbound

This paper cites The features with a lower resolution gradually increase the resolution by convolution layers and then combined with higher resolution features.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 The features with a lower resolution gradually increase the resolution by convolution layers and then combined with higher resolution features

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.061524Z

Source-reported events for the cited work

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Observation da178ac3-e3fd-4041-bb84-6152d9cacb56 · outbound

This paper cites We set the initial learning rate to 0.001 and employ a “poly” decay strategy in Eq.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 We set the initial learning rate to 0.001 and employ a “poly” decay strategy in Eq

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.049684Z

Source-reported events for the cited work

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Observation c6dca26f-2b09-4143-bfb0-014921739f80 · outbound

This paper cites With the two re- finements, the results clearly illustrate the progressive im- provement in segmentation accuracy, emphasizing the ef- fectiveness of our model’s refinement process.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 With the two re- finements, the results clearly illustrate the progressive im- provement in segmentation accuracy, emphasizing the ef- fectiveness of our model’s refinement process

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:21:46.037840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.