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Paper Citation Record · LEDGER

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

As of 10 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 2026-08-09T06:31:02.800959+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:ceee30ddb2d74128f1c83852d91c1d79cd49f59c55251ce5c63d2575f02c8e07

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.666189Z digest=sha256:e322dc928db20362b5c19c4bf7873664a6cc0b37da724eece1d5e5e435a20df7

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.670152Z digest=sha256:dcff92292cd1dda53b0d505db4e3f8a1b687445d5935b7bfff8c5b27343f8366

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.674053Z digest=sha256:ec5bb36af397ac6694d85045ab9079a0a72ad2ef109d8941e9bdc98a74d1c322

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.677611Z digest=sha256:8f2d626a417ebdc10f9f3e51e02cce116b1ba7b8f0a26e8b06d7e622417375b5

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:e58fdecf60f5338255ccac6452abf554d8cbc522202eceddcf5deb8e3ae7b90a

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.685247Z digest=sha256:c6a6bf4a3225f4361f1327cae218db2d7272dbaa05df65f800546c4cbec0ff89

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.689915Z digest=sha256:82e89dbd5e4cc29b6b2bf4bcc54db329f888f0c50de817ddd54cf4afb39a3992

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:ece10aaef9def73820b5d0294deddd587224eb35e672124dcfd4716a0b578bc6

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=sha256:124ebe129675b81b2d194ded35b5761b023eb8f50942d6f1810f72a1d2764c01

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.701751Z digest=sha256:e96d1c5faed6066f09c62a87fc9395bb8f8aa84b7cad57f0b7f541c3ccf9f197

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:10:42.705469Z digest=sha256:e6a0c3b4574979624c069545458210a2bb3eaa892d1376373a269bb7ea782657

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.709205Z digest=sha256:d49b44a0a8d484c305137fc508194d92a5a831f10d8e7f50b8786a8e860b6e4b

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.712910Z digest=sha256:2c585f60c00211256121beebe7a721fe7ff9afcb8acf7b600329bb38f5e59d9e

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=sha256:6f0bced694eb5c9a978ed9bc78b2459d563a73645ee5f90faf91852c55d35806

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.721311Z digest=sha256:7a83fe617bc9b997c4a9115e0bffda6adbf1740430678c52b751f5612ba0f999

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.724545Z digest=sha256:2597369a0af8338b10c81e785efb4203376778575b68049843addf9f29d8796f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.728586Z digest=sha256:eb2988f19256feaa3d6ce4a81c8b975f47312d454c73d381850b236a3aa3bcf8

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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raw_fallback, observed 2026-08-09T10:10:43.388848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.732402Z digest=sha256:216c601674ed0f1a997b693b4a06c6ff80bfc35aced0814581cb45eb4f422977

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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.735566Z digest=sha256:ab7e7244f9b75a5ac2c52880654dcba5674a05fc405716ca9d601527eae69bab

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=sha256:2201a3c7b8db30eb276b6a9a4469e20968f52e1228c1c812bc7439d1dd7f527c

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=sha256:28a470a31229791467e3021e6489a326eebdc1efacb525f0890429282c0c74d8

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=sha256:69ff00c9f74c432a3367c1963192524076e72eaf322b319fecc70783e94b12bb

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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.751385Z digest=sha256:18a113afb2f9ea93c8d40874e0e5a906f56c0ffcb381194ad949c027d7e15f65

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

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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=sha256:7f1a1cd059f8d186c711a07acb168adfbf394f1d43cb5f44283acbfae721af7e

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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.758732Z digest=sha256:7c71eaf1df5e1d0a69242416ff2745136165dd33a0e29c0fe9d62133784585d9

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:aeb5a9d6d38d24bc308455b1bf617096f00ef014d5aa24a4e022cde23b591784

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.766079Z digest=sha256:995534104b8900fde459ebf6b00b581f18ae1a52c482bea9a5552ece04ef3471

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.769725Z digest=sha256:99afddb166f163a57b2f5115c5ee3665eadc92f8e5438daefc64ecd4194d60cc

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=sha256:222a944cd6b65cb3e4d5a26ab5c89d78317e44f171bf6a9e235e33a0122efba8

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.777167Z digest=sha256:ff978c645980325cf04b0013758525d45cb324a2426e0b0a60087e9257a08493

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.781839Z digest=sha256:8a886ac73736eaa80c98cdfaa9758e1aa1191f1751a7bee3b6697168434219b6

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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.785623Z digest=sha256:7e6244872eba1e9b2be703d133b5b016e3ccac92d98f30e82c1eed3e10e0d124

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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.788947Z digest=sha256:8e52c70f814360a922aedf7f58ead4420d80511f427cf8ec6c3ba65e6f22b541

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.795949Z digest=sha256:838c9230ef4798f072d7e6d442b42ad0d0891763fb7c41acb3c7bad6f1e9a0fc

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.799530Z digest=sha256:21497075df48b118382fc35c0248be4640ac14e3a7e1992dcb0f2ff20be98474

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:bed6c6fc9e2aae3bda3169656fe86d8b6021e6144d1c7b8a4037e31a1a9e527f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.806351Z digest=sha256:d32674e7268ab616c0ff1ded253a2c73d215ebbac0de7c1e3d4de10edc983320

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=sha256:9bf9731e6df778897586117df0c06cdb10b20d60a039aaa6e364f1f36ea44f49

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=sha256:79682198fde1993893276411010be6ef7a0f1482560ffd5aadb9b7845b87a647

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=sha256:9fdd3819f3c56a418b35032c36cee03d70de6bff8945db05857b1fcf36cb8078

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=sha256:1fc0e47abcb8a6e9e47d9d5d251106477dec9612c39bf02fd9aa0721736e748a

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=sha256:6dd273b5f2269119564461f4308bf2c6c8196cb28bc4e30c865da3f304323f35

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:fa2bb24c4b845065c082431c2be93725d1dd49467809c9dea7ab914422f7d7b1

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.832190Z digest=sha256:950c32ef91ad5eef328513d161a07dc7985e9462cbdc8b7179cd1495bbeb5ae2

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.839054Z digest=sha256:72fef7fc1226f29f520992188d7c958de204bde573590535ecd36086b8b10134

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.842664Z digest=sha256:f49c48b9b9d7a896caf332e7de01686f8cd82b1932999853973669ec21f8c1ae

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.849547Z digest=sha256:fafbf6426a89d9c6c1ce75265da8195023fe9fbf54d3e2312b6d5f2d1a48f542

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.853459Z digest=sha256:b131309c306a8707c6a9e3726e42b0ef73f65227500e8c68b279b2e6654f4fe3

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.856759Z digest=sha256:5124ae8bce312762055fcccce4d0c26962c43b0028db970533219868fc3e9bfc

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.860258Z digest=sha256:7610dc9f419f65cefc35f682cb97f2034ac2f11049294cde0e1f51ba05a4313f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.863530Z digest=sha256:c7c97768243252238f2b4f829395727de855a3d48d9276606d07a6d9f00d7074

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.866943Z digest=sha256:a3e618b2be6d2ef68cf76f78c420734fd9b4f58ee1e0b0ffc89bd8a8a1e03e0b

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.870795Z digest=sha256:51ce1fcfb9d8f779a6affc244e4abc9ea16f93ea031b50ba7f122444c5a70f7f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.874408Z digest=sha256:8891e087a226acf53e6aa2b7fbb02d6754b365f0e481a35ab1001a599acb4851

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.878210Z digest=sha256:4657de8a8cec5ddaeddbafab531a2efb449712f4e20c0e3a33e2f7c169824963

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.881663Z digest=sha256:8c3e479484314a642b1bfd4319712bcaea684b15d854feb649160561cec1f504

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.885284Z digest=sha256:834b45ae0d017715621ec5b9b842a6e6f60056d9b178145a77fe43043045413c

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.889547Z digest=sha256:44c9b43995236496415ef8a499f44af8f20cb97ef627d5987ca17973d95571cf

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.893230Z digest=sha256:6b83f0ace1a98d31f4364f332436bb0c68e8094164fccb971242f453fc21025a

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.896417Z digest=sha256:0548f06c45ba07960a1547280bd7937589584a173810c27a2deb957dcfb64295

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.899627Z digest=sha256:c37a6b62eb283918fa8be4c2e4009e2f825280fd32b3aba55c66ca3d8dc1294d

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.792607Z digest=sha256:d83ac5936cdffcb08f345a9bf4321d9c7b9865cd5da943ac5261b848fd769412

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T10:10:42.835482Z digest=sha256:d776e195a530a9c4458368a0ebe27b539688c2ac08cce730544714a011cf8880

Pith citing papers

No inbound Pith citation observations are available.