Paper Citation Record · LEDGER

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting

As of 13 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 2 inbound Pith citation observations for arXiv:2411.17363.

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

pith.paper-citation-record.v1
2411.17363 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:16:19.081312Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:56:03.789703Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T18:23:51.202014Z

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy19
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1b17b434-0ba4-428a-96df-4698edfae9e1 · outbound

This paper cites Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.357978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 23665588-cf2e-4835-b481-2db54c1221b8 · outbound

This paper cites SAM-Med2D.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting SAM-Med2D

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T12:16:18.991674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb0628f6-4400-4f8e-ae90-14db58f431a6 · outbound

This paper cites Few- shot medical image segmentation via generating multiple representative descriptors.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Few- shot medical image segmentation via generating multiple representative descriptors

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.346903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0bfd917a-e16c-4879-b9f9-a30b68c73ad2 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T12:16:18.998514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9b65320-5573-452c-8498-4375f6fd4b02 · outbound

This paper cites Efficient graph-based image segmentation.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Efficient graph-based image segmentation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.337957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation aa40418a-7595-4acf-b199-3f4832871c1a · outbound

This paper cites Interactive few-shot learning: Limited supervision, better medical image segmen- tation.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Interactive few-shot learning: Limited supervision, better medical image segmen- tation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.328036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 06dfaa1b-dab3-442f-be50-935fa6e4783a · outbound

This paper cites Bidirectional elastic image registration using b-spline affine transformation.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Bidirectional elastic image registration using b-spline affine transformation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.317553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 10396155-8efc-44f8-9df0-8422f0b3f1cf · outbound

This paper cites Anomaly detection- inspired few-shot medical image segmentation through self-supervision with supervoxels.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Anomaly detection- inspired few-shot medical image segmentation through self-supervision with supervoxels

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.308089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 55c73a74-309d-4576-b223-994bcce425e4 · outbound

This paper cites Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T12:16:19.015544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1a3f06c-207c-403d-b8c0-33f2390aed90 · outbound

This paper cites Auto- matic tuberculosis screening using chest radiographs.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Auto- matic tuberculosis screening using chest radiographs

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.292208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 35a0c646-fc3e-4547-a764-1240b41c9f03 · outbound

This paper cites Segment anything.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Segment anything

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T12:16:19.021377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a1f9afe-f1f7-49f1-b94c-fda70eb221d7 · outbound

This paper cites Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T12:16:19.024296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae26b3fb-686a-4686-8888-0023c0390553 · outbound

This paper cites Segment anything in medical images.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Segment anything in medical images

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T12:16:19.028141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b067bfd8-3a6f-4388-bed8-8cb92daf56ce · outbound

This paper cites Self- supervision with superpixels: Training few-shot medical image segmentation without annotation.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Self- supervision with superpixels: Training few-shot medical image segmentation without annotation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.272093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 480d99fa-21ee-4059-b198-b468b53cda9d · outbound

This paper cites Comprehensive multimodal segmentation in medical imaging: Combining yolov8 with sam and hq-sam models.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Comprehensive multimodal segmentation in medical imaging: Combining yolov8 with sam and hq-sam models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.261995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6811c491-7bda-47fc-ae8f-ad70a07497c8 · outbound

This paper cites Adaptivesam: Towards efficient tuning of sam for surgical scene segmentation.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Adaptivesam: Towards efficient tuning of sam for surgical scene segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.252428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9014c3fc-87ff-4501-8cff-d125ce1460dd · outbound

This paper cites ‘squeeze & excite’guided few-shot segmentation of volumetric images.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting ‘squeeze & excite’guided few-shot segmentation of volumetric images

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.242928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 084ee6d4-e19a-492c-8fc2-895604b6f15d · outbound

This paper cites Prototypical networks for few-shot learning.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Prototypical networks for few-shot learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.232372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 79c39fb6-91c5-4da1-ba1a-f6842a74495a · outbound

This paper cites A com- prehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting A com- prehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.223119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7695dd16-9558-4770-ac82-8aa66feaae54 · outbound

This paper cites Few-shot medical image segmentation using a global correlation network with discriminative embedding.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Few-shot medical image segmentation using a global correlation network with discriminative embedding

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.213199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 184ed2e2-b3c4-41e1-8e69-6d1d801710e7 · outbound

This paper cites Medical image segmentation using deep learning: A survey.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Medical image segmentation using deep learning: A survey

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.203456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e452bc5b-dca7-465e-af26-9a087128e18f · outbound

This paper cites Few-shot medical image segmentation regularized with self-reference and contrastive learning.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Few-shot medical image segmentation regularized with self-reference and contrastive learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.194035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 168e1b63-d215-4d50-af53-a78234e84e3d · outbound

This paper cites Generalizing from a few examples: A survey on few-shot learning.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Generalizing from a few examples: A survey on few-shot learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T12:16:19.061468Z

Source-reported events for the cited work

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Observation ca118ccc-646b-47cd-8fa2-189182bc692b · outbound

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

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T12:16:19.064569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21508ab1-16c7-4259-9e89-7170d326c221 · outbound

This paper cites Itk-snap: An interactive tool for semi- automatic segmentation of multi-modality biomedical images.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Itk-snap: An interactive tool for semi- automatic segmentation of multi-modality biomedical images

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.179539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4eeb6f48-6327-4587-979c-f2e4f804434e · outbound

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

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Customized Segment Anything Model for Medical Image Segmentation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T12:16:19.070786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e32e1d8b-8c83-4b1b-8c93-4740f4060c03 · outbound

This paper cites Personalize Segment Anything Model with One Shot.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Personalize Segment Anything Model with One Shot

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T12:16:19.074930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 637dc8dd-dfe5-4957-9c99-9447893ecdb3 · outbound

This paper cites Segment anything model for medical image segmentation: Current applications and future directions.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Segment anything model for medical image segmentation: Current applications and future directions

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.170039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3511f30c-d034-48f8-8141-71c8683269c1 · outbound

This paper cites What makes good examples for visual in-context learning? Advances in Neural Information Processing Systems , 36:17773–17794, 2023.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting What makes good examples for visual in-context learning? Advances in Neural Information Processing Systems , 36:17773–17794, 2023

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:16:19.159583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation aac91243-ab07-4011-afe8-9673e62f9d9d · inbound

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges cites this paper.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.789703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63e82911-f7d5-4cf6-bbe8-312707a06f09 · inbound

TRUST: Efficient Abdominal Trauma Recognition via Image-to-Ultrasound-Video Transfer Learning cites this paper.

TRUST: Efficient Abdominal Trauma Recognition via Image-to-Ultrasound-Video Transfer Learning SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:23:51.203325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.