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Paper Citation Record · LEDGER

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation

As of 16 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2502.03501.

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

pith.paper-citation-record.v1
2502.03501 v3

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:10:42.899627Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

64 of 64 outbound references displayed

  • verified exact0
  • verified fuzzy35
  • unresolved27
  • parse uncertain1
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 14ec6b2d-be57-49c3-88b2-d5b3453942ff · outbound

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

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.661544Z digest=sha256:360e0959308304e799c635499caa848ac08dcc2de0bdfa95260cf96ae41cb8aa

Observation 442ea059-ff64-49f6-bce4-fbad1c93aa78 · outbound

This paper cites Segnet: A deep convolutional encoder-decoder architecture for image segmentation.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Segnet: A deep convolutional encoder-decoder architecture for image segmentation

Reference 2

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raw_fallback, observed 2026-08-09T10:10:43.513959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.666189Z digest=sha256:f56fe686f1517632674248c1cde4caf955abbc65eb7b1832c5829847c954807e

Observation e8df7f84-649a-41b7-be73-3474d5959561 · outbound

This paper cites Uni- verseg: Universal medical image segmentation.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Uni- verseg: Universal medical image segmentation

Reference 3

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raw_fallback, observed 2026-08-09T10:10:43.503171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.670152Z digest=sha256:39bdccb65843ff4059a48c11a9bf96ef9aff91890f7f491b3d78e4fafcfb851d

Observation 12c0fd30-0c69-43f7-8047-8fbe67e41fc4 · outbound

This paper cites Seg- mentation by registration-enabled sam prompt engineering using five reference images.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Seg- mentation by registration-enabled sam prompt engineering using five reference images

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.674053Z digest=sha256:d85ef5e257763ee485ae27bd70fe3529609db52cbeb530fbf0c0ea141c20069b

Observation 620c5b8b-8d10-4f98-808e-1d6809d98dd6 · outbound

This paper cites Rethink- ing space-time networks with improved memory coverage for efficient video object segmentation.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Rethink- ing space-time networks with improved memory coverage for efficient video object segmentation

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.677611Z digest=sha256:a60c9b3ac57b15557664f4d02a24ab34a5fea23a5e754b46578a653d590d8f29

Observation cc499de2-d45e-4400-b5f5-8ae87d89fa43 · outbound

This paper cites REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.681478Z digest=sha256:474e4fa29d73cf763c4642e1e476732cc61a3800ce588f8d4147bee918ab7522

Observation 22ce41c7-ba2e-443f-8aea-9ea25d32ef16 · outbound

This paper cites Isuog practice guide- lines: intrapartum ultrasound.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Isuog practice guide- lines: intrapartum ultrasound

Reference 7

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raw_fallback, observed 2026-08-09T10:10:43.474393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.685247Z digest=sha256:daeb82e185b3ff05cc2a5952ff9f9b9c0cf75e143b236a3b5fc7b272a39cb372

Observation 7ede956f-622c-4252-9825-3bf62cc9ad9f · outbound

This paper cites Modeling Sequences with Structured State Spaces.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Modeling Sequences with Structured State Spaces

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.689915Z digest=sha256:ca69716b185fda3ad9360861c8a90cd28d3e85e928510bbaeb932537d358ebe4

Observation f1e0ed1a-96cd-41ac-9d08-761879350f04 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.693359Z digest=sha256:6e2d11297e5374c837c94f1440fcb2f9c40694e3fef90e8c7c6bb11afc951109

Observation 986b0c75-2b6c-4059-9c56-9b9b37a7f195 · outbound

This paper cites Deep residual learning for image recognition.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Deep residual learning for image recognition

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.697159Z digest=sha256:608ad847e445a0f4c8954bdd353a7a235273294f174d3557caf1a79cd26c9b85

Observation c116b31a-e311-46ef-bc7b-c8c39cfa631e · outbound

This paper cites Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.701751Z digest=sha256:0fe0d81f1c925a05eebb3e66c9d9e1c127357b943acc9828b53e2d361b14f016

Observation 0e884586-eec9-4718-854b-cf93ee9ee6c8 · outbound

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

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation LoRA: Low-Rank Adaptation of Large Language Models

Reference 12

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no resolver link, observed 2026-08-09T10:10:42.705469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.705469Z digest=sha256:0c8ffe99ed6dd7e980481ad203ee28f2cd16bb07d5bcb6c735bcdef7472b1426

Observation c9a3ccea-bd0c-43f1-ac28-8e3b8f7e4ad1 · outbound

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

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation

Reference 13

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.709205Z digest=sha256:bcc2034969d3ed0f0d2101c68dc45c9ed66251b2d4139e9610e83f89ebc54438

Observation a463c361-0fbf-4c7a-8675-b1e8ba9b0459 · outbound

This paper cites Pubic Symphysis-Fetal Head Segmentation and Angle of Progression, 2023.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Pubic Symphysis-Fetal Head Segmentation and Angle of Progression, 2023

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.712910Z digest=sha256:384f4c5a84eca28a2b835ca593d2c10809d561ac0840bf448cd8f9c9580f8968

Observation 17a60cbe-5be6-4987-83a4-fb58214de8da · outbound

This paper cites VM-DDPM: Vision Mamba Diffusion for Medical Image Synthesis.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation VM-DDPM: Vision Mamba Diffusion for Medical Image Synthesis

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.717492Z digest=sha256:486e171f817c7cd1a965643890c28a6aca4eeacb1a77ef2ea9a88f6c317ad367

Observation 440103db-632a-4c6a-80a7-78b6e6c91ae9 · outbound

This paper cites Segment any- thing.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Segment any- thing

Reference 16

Resolution
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raw_fallback, observed 2026-08-09T10:10:43.419634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.721311Z digest=sha256:5a96e6eb92b488ba01846c8acea9db30abc7310e259c672cb2ca3bfa00292469

Observation 9997a0a6-a13b-4247-831e-d7c92c0359c8 · outbound

This paper cites Visual in-context prompting.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Visual in-context prompting

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.724545Z digest=sha256:57978946d4524a37a1e05bf09b8e68bfdddccd0813e2eccf13aa86f512fdca6a

Observation b8029d65-750b-4a94-b9d1-e6424b191d63 · outbound

This paper cites Evaluation of prostate segmentation algorithms for mri: the promise12 challenge.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Evaluation of prostate segmentation algorithms for mri: the promise12 challenge

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.728586Z digest=sha256:f16dca416a8b1f586ca18bce193dfdab968540e8953985fdba28763cff47508d

Observation ffaf2f62-0907-4c4f-9894-c08a28baef17 · outbound

This paper cites The jnu-ifm dataset for segmenting pubic symphysis-fetal head.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation The jnu-ifm dataset for segmenting pubic symphysis-fetal head

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.732402Z digest=sha256:54ca5ddb588d56192d8f65d4ce55aaed4eec53550dbaed098e47cb54132cef0b

Observation 47fa46c0-65f2-43c1-9efd-238f470b759b · outbound

This paper cites Semi-supervised medical image segmentation through dual- task consistency.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Semi-supervised medical image segmentation through dual- task consistency

Reference 20

Resolution
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raw_fallback, observed 2026-08-09T10:10:43.379260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.735566Z digest=sha256:b735604265d8237df57a2aaf56cc21e41ec27702bad69406adfe33d316f2ce85

Observation 65b31dd2-233e-4611-aa88-a47fa2187992 · outbound

This paper cites FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.738764Z digest=sha256:faba41e0ed2f488c110b9feae47f6743dfb389f4f36a1a6eecf33bdda1b38eb1

Observation 38a464f6-6609-4d40-92b0-46430e925f4a · outbound

This paper cites Segment anything in medical images.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Segment anything in medical images

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.742560Z digest=sha256:bb66f55bb2d6b0977902189ef9715197692c1d926c8f17e78410f025a589cdf8

Observation dfb2bb39-cffa-4096-8ce2-1d9fa75b26fe · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.746727Z digest=sha256:a2e0f5a2641e0e232c3802be3717852c2853def96d6558a88f9768edac21562e

Observation 1a38660f-b786-4a44-8f11-aec1dd6a1840 · outbound

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

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 24

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.751385Z digest=sha256:b1033b22174dc8da9ff57c181c310c555e37331315ce9a35997671946fc8259b

Observation 01f17d5f-35d8-40ca-a679-0223f16ccafb · outbound

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

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation SAM 2: Segment Anything in Images and Videos

Reference 25

Resolution
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no resolver link, observed 2026-08-09T10:10:42.754773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.754773Z digest=sha256:b318ab3162a706117032a9950358eaa1931a555204ab207f1fc57b87af80d156

Observation cc97396d-21ed-4a8b-93ba-ed21a2d0797b · outbound

This paper cites Auto- mated localisation of optic disk and fovea in retinal fundus images.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Auto- mated localisation of optic disk and fovea in retinal fundus images

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.353765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.758732Z digest=sha256:c31c04600413ceb1c47a812e6dab06c67269d217ab5ac5a6d8e48212e39b68b7

Observation 1ea2fad7-f99c-4211-ac34-278f6aeeb983 · outbound

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

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder

Reference 27

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no resolver link, observed 2026-08-09T10:10:42.762365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.762365Z digest=sha256:28b2b68765da8967e776da2fc950d39d015ff5d21b616af1b428e2e9cb0285ae

Observation a4db26bf-13a2-4827-b979-02d08e917134 · outbound

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

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Vrp-sam: Sam with visual reference prompt

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.343975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.766079Z digest=sha256:3a8097b632a768868be8d76852d076a1c0c04864ce5854df9d3213558edf4f1b

Observation 4071ef4a-3567-497c-b266-1c53a3428009 · outbound

This paper cites Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma

Reference 29

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raw_fallback, observed 2026-08-09T10:10:43.334440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.769725Z digest=sha256:d8adf0e36ba403c0d5a664c8b9940c22f919337beb07b38ebbb3c8ebefa0b9ab

Observation 3f44fc3d-4dcd-4c1b-9c45-2c32d228f806 · outbound

This paper cites Attention is all you need.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Attention is all you need

Reference 30

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no resolver link, observed 2026-08-09T10:10:42.773615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.773615Z digest=sha256:e0aded117a1a4156bf0eaaf30f170fde7c98776e75d19aeed8de26e23a9a23cb

Observation 2fafebf9-ce43-4e01-b34a-4462127c9966 · outbound

This paper cites Deeply supervised 3d fully convolutional networks with group dilated convolution for automatic mri prostate segmentation.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Deeply supervised 3d fully convolutional networks with group dilated convolution for automatic mri prostate segmentation

Reference 31

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raw_fallback, observed 2026-08-09T10:10:43.320314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.777167Z digest=sha256:01b984d577ab77d6b7555b1c84b6c1f9bec37c47e21a2d5ec0648e3776524262

Observation 895ead5c-c745-4448-a252-9cff3642445d · outbound

This paper cites Review of large vision models and visual prompt engineering.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Review of large vision models and visual prompt engineering

Reference 32

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raw_fallback, observed 2026-08-09T10:10:43.310909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.781839Z digest=sha256:e48629801f35c43a16cd7568ce7b75de1c7bdbf0179227f5ed58310dd0a32b6b

Observation 33ffb6b1-668d-447a-b70b-7a2151e10502 · outbound

This paper cites Boundary and entropy-driven ad- versarial learning for fundus image segmentation.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Boundary and entropy-driven ad- versarial learning for fundus image segmentation

Reference 33

Resolution
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raw_fallback, observed 2026-08-09T10:10:43.302029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.785623Z digest=sha256:5248322fa9a281571d590f2196b7c08a00bbf73b2948af91f34325d1bc1b8a9e

Observation b6e40a45-53d0-4f81-83ae-0c6ddfe20bb2 · outbound

This paper cites Consistency-guided meta- learning for bootstrapping semi-supervised medical image segmentation.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Consistency-guided meta- learning for bootstrapping semi-supervised medical image segmentation

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.292391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.788947Z digest=sha256:ccdb2586d325cdc811e6de0940587ae45aaa36f41cdb1ccc9349ea83bec04e47

Observation 89953310-8e3c-439f-9c70-107a8fd1885b · outbound

This paper cites Cbam: Convolutional block attention module.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Cbam: Convolutional block attention module

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.274055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.795949Z digest=sha256:e0ccd45adf3a1d92397b4245588d42b5e73e4094277c41b27ded1434c4d39f21

Observation 6ee6a43f-e4a3-4ac5-a717-f2745825f4c3 · outbound

This paper cites One-prompt to segment all med- ical images.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation One-prompt to segment all med- ical images

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.265115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.799530Z digest=sha256:f1d07b391549b58cd40d84cd0a7e66a5f180586769387a717e5bcfe8dff7093e

Observation c5fd905f-8e00-4c57-be4c-42c5cb55db63 · outbound

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

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-09T10:10:42.802795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.802795Z digest=sha256:cc395000c64aedd3d88bfd42c846285fb154750af2b73cb263512581cbf84351

Observation 0eb68023-507a-4172-aad5-575612fd95a9 · outbound

This paper cites Self-prompting large vision models for few-shot medical image segmenta- tion.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Self-prompting large vision models for few-shot medical image segmenta- tion

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.255903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.806351Z digest=sha256:929b5eb085a69dffa90e23470a60884db8af56be7149ade5e456ab586bc803d7

Observation 796231c6-c561-40ed-98cf-7e06ee204137 · outbound

This paper cites EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-09T10:10:42.810240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.810240Z digest=sha256:be1992351d3efd6cf4e70f2eec837d2de29ac714907d2e79c468220798242905

Observation a9400399-fe55-42ec-883f-370a66744e76 · outbound

This paper cites TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-09T10:10:42.813831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.813831Z digest=sha256:673d3979ddc487858e6b06ed8052d6ddf51570abb1b9d5a80e4a234b6a780a57

Observation a70168af-6f70-414f-833f-d7ab863a68df · outbound

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

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Customized Segment Anything Model for Medical Image Segmentation

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-09T10:10:42.817574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.817574Z digest=sha256:b42f4d5f8347903d0ba13b0851ab8d426b611a7fe0844fee0c97740fc07bcf59

Observation 30b9c05e-873b-4aa5-84df-24eee15462a8 · outbound

This paper cites Personalize Segment Anything Model with One Shot.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Personalize Segment Anything Model with One Shot

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-09T10:10:42.821410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.821410Z digest=sha256:1eb0177897c0e2daf511c274362f1c3566bbe38576d46ab5fe718cafec3be8da

Observation 5cd74614-0b91-4ae3-a099-e58c105e4fc6 · outbound

This paper cites FD-Vision Mamba for Endoscopic Exposure Correction.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation FD-Vision Mamba for Endoscopic Exposure Correction

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-09T10:10:42.824747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.824747Z digest=sha256:72d1e6a6f0b4b090a11730724a88e345a0023ecd6fae11ac45ca00b66249ef07

Observation 90dbf59e-6503-470b-a4ab-2209c0385b4a · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-09T10:10:42.828293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.828293Z digest=sha256:47235d8b712e07ebf22a657b773622fe5901c99c0fde9d39fb8a2284e7a15579

Observation d388a60a-4dcd-4229-8252-a16557d382d9 · outbound

This paper cites Segment everything everywhere all at once.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Segment everything everywhere all at once

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.246404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.832190Z digest=sha256:1055cfabec6d987c327ab093192973f755ac715186cedabcf06c96a388c3d067

Observation 3a23929f-861d-4fb5-b3b2-dfb6951340aa · outbound

This paper cites Settings for Image Dataset.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Settings for Image Dataset

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.225077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.839054Z digest=sha256:37e22950d8126feeac5c6774ee379d090bac79f8db8e13e5bf24075bdea4b092

Observation 79f52cd8-fc24-4152-a33d-71d6d8ebcea9 · outbound

This paper cites an unresolved cited work.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-09T10:10:43.214219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.842664Z digest=sha256:dae90683de90b1e5eea80fa407b860c2258c3b508776421608e49494a3bd5998

Observation e22079b1-842d-4e51-94a9-66283a77b19d · outbound

This paper cites We select the prediction that the highest overlaps with ground truth to calculate the model’s Dice score.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation We select the prediction that the highest overlaps with ground truth to calculate the model’s Dice score

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.193862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.849547Z digest=sha256:76a44c745b92cef217717a849d41af4aeccb342f21be6f14f76974b08930e848

Observation f9b43eb1-6636-4532-8433-58b8e5e258fd · outbound

This paper cites an unresolved cited work.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-09T10:10:43.184433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.853459Z digest=sha256:43fdfe8ff9fe7812122dc2eeca25bf5a2bcdeb13e660ebe83d3ca6274faddd1d

Observation dd6ad7a9-39af-4778-8210-bd35482bc157 · outbound

This paper cites an unresolved cited work.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Unresolved cited work

Reference 53

Resolution
unresolved
raw_fallback, observed 2026-08-09T10:10:43.203929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.856759Z digest=sha256:edee247c9d802371f6d1a6f971f053b44c5ffb9f522afb0ff36c7568629f6702

Observation 16e23052-c9fd-4894-b34b-e2fabfdf2554 · outbound

This paper cites an unresolved cited work.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-09T10:10:43.175230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.860258Z digest=sha256:000bc0b3638a992cdbbf2529bc83c2dc488ecd59128c6c9ed35a961540e49029

Observation 4f4c7126-ebf0-4862-8263-32004105916d · outbound

This paper cites an unresolved cited work.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Unresolved cited work

Reference 55

Resolution
unresolved
raw_fallback, observed 2026-08-09T10:10:43.165911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.863530Z digest=sha256:ae2106bd8aac35618fca127fd2ebcae0c5acfa9002c1d28ab060021291527e08

Observation 3b4645b7-f003-4297-a63c-bce23396a0b6 · outbound

This paper cites non-target.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation non-target

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.156696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.866943Z digest=sha256:c8a9ea03b94ef852bbf82ba88b453c4ae841d2424d0b9de4c0f45f5e0a8dff77

Observation c5f21b20-4bbd-4f72-9712-bffdc16b909c · outbound

This paper cites an unresolved cited work.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Unresolved cited work

Reference 57

Resolution
unresolved
raw_fallback, observed 2026-08-09T10:10:43.146952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.870795Z digest=sha256:217042521b304ed33ece52c1ef44c2e0468cd7a07549c14785e8fb85cd6ff6ac

Observation 039f6bfe-4901-4a6a-a789-86782e4f435a · outbound

This paper cites 3D DSD-FCN [31] and MLB-Seg are existing SOTA models for PROMISE12 [18] dataset under fully supervised and semi-supervised settings, respectively.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation 3D DSD-FCN [31] and MLB-Seg are existing SOTA models for PROMISE12 [18] dataset under fully supervised and semi-supervised settings, respectively

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.137265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.874408Z digest=sha256:eaed1bc8029963d5b5a4a60649c0fea433787078a883d4bedd2cca292e485a8a

Observation 8f301f2d-db13-447c-b237-45b52e6a88b6 · outbound

This paper cites Given that SAM2 is pretrained on a large-scale video dataset, it was a natural choice to integrate our method into SAM2 for video segmentation.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Given that SAM2 is pretrained on a large-scale video dataset, it was a natural choice to integrate our method into SAM2 for video segmentation

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.127777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.878210Z digest=sha256:478678aedb3d9da3380382fbb0a768009d321b787f2e31827d0b3c75dd0370a9

Observation ce848969-9b69-42df-8be8-6a3abe0e0500 · outbound

This paper cites #𝐹𝑟𝑎𝑚𝑒!"$%𝐹𝑟𝑎𝑚𝑒!.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation #𝐹𝑟𝑎𝑚𝑒!"$%𝐹𝑟𝑎𝑚𝑒!

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.117671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.881663Z digest=sha256:5873d757cc5444d6c5c8c38a7c8b3a569c382ce896063aef1443ffef30840073

Observation 07f175ae-f5d3-438b-ae2c-f5621cca7d40 · outbound

This paper cites For our proposed modules, we conducted ablations on CSM and CCM, visualized the effectiveness of the Selec- tive Map , and analyzed different encoder architectures within CSM.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation For our proposed modules, we conducted ablations on CSM and CCM, visualized the effectiveness of the Selec- tive Map , and analyzed different encoder architectures within CSM

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.107143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.885284Z digest=sha256:c1995437fdf2238dfce7de3ff0a17c23ad7bd815eede23c8d8f8775d5f27c54a

Observation 2b64b74a-8d34-46e8-9da0-3ff252903975 · outbound

This paper cites an unresolved cited work.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Unresolved cited work

Reference 62

Resolution
unresolved
raw_fallback, observed 2026-08-09T10:10:43.097275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.889547Z digest=sha256:f2a221c91e4f1e884e73a1e3deef08cf7e56548331092ef88b68fa27a4daf05a

Observation 05e2e20c-5350-4dcf-a9f3-96f6612b3aec · outbound

This paper cites The most notable improvement is on STARE-Vessel, where Dice increased by 39.3%.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation The most notable improvement is on STARE-Vessel, where Dice increased by 39.3%

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.087244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.893230Z digest=sha256:487adcabf2751282fccc5bc404d5e8144dca31bb5cc38817c3129aaeb94e8a01

Observation d6a1fa2e-45fc-4ac1-b2db-40e82a936e05 · outbound

This paper cites According to MedSAM’s supplementary mate- rials, its pretraining included FPA and REFUGE datasets.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation According to MedSAM’s supplementary mate- rials, its pretraining included FPA and REFUGE datasets

Reference 64

Resolution
malformed identifier
raw_fallback, observed 2026-08-09T10:10:43.077930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.896417Z digest=sha256:f9df14ce060d25a4eeb2c42d064d8c0c876e2205156618321dc7357fc0ff84d3

Observation 1d516bd0-2fe6-47bb-b14a-8ccd57307fb5 · outbound

This paper cites an unresolved cited work.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Unresolved cited work

Reference 65

Resolution
unresolved
raw_fallback, observed 2026-08-09T10:10:43.066375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.899627Z digest=sha256:03b68994f2ebbfc08f6c9d295688099b7c1ac766db6775d1853a017bc02f0767

Observation afd7f613-9ae8-417a-9db7-196c7f074e03 · outbound

This paper cites an unresolved cited work.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Unresolved cited work

Reference 193

Resolution
parse uncertain
raw_fallback, observed 2026-08-09T10:10:43.283852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.792607Z digest=sha256:2b0994461fe4bbb7260b0c26cfb8b5913bb591ab510ed0b8992d198d2d38721e

Observation 125fdefc-2664-48dd-807c-75109c3661d3 · outbound

This paper cites Quantitative comparison results on four representative examples.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Quantitative comparison results on four representative examples

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:10:43.236506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-09T10:10:42.835482Z digest=sha256:084bd9713999ca26235d604832ecc3119dde3763fa0c3134e890c08159bc21b5

Pith citing papers

No inbound Pith citation observations are available.