Paper Citation Record · LEDGER

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations

As of 11 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2501.18474.

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

pith.paper-citation-record.v1
2501.18474 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T23:24:39.284094Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

32 of 32 outbound references displayed

verified exact1
verified fuzzy20
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f2cccac5-cc09-4d04-a360-6dbec2c81d0a · outbound

This paper cites Long-term outcome after stroke: does dysphagia matter?.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Long-term outcome after stroke: does dysphagia matter?

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.695976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c4afa2e1-16c6-4c9b-8b2b-e58acea22103 · outbound

This paper cites Dysphagia: A geriatric giant?.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Dysphagia: A geriatric giant?

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.684126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c45b66b6-fd20-4368-a042-1e50e0393c3d · outbound

This paper cites The natural history of dysphagia following a stroke,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations The natural history of dysphagia following a stroke,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.672429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9f71ed0c-d495-4d5c-b0df-84924f807a6c · outbound

This paper cites Early assessments of dysphagia and aspiration risk in acute stroke patients,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Early assessments of dysphagia and aspiration risk in acute stroke patients,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.660829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c0c87500-0f69-48d9-9ba7-20c1d014de49 · outbound

This paper cites Segment anything,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Segment anything,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.649873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 08f59c59-8db7-40b1-80fc-5f0b85010954 · outbound

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

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations SAM 2: Segment Anything in Images and Videos

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T23:24:39.183549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0e2d4aa-cb96-453d-a16b-3a4d9643f52b · outbound

This paper cites Segment Anything in Medical Images.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Segment Anything in Medical Images

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T23:24:39.188724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75ba1e71-c53f-415c-9cde-bd5b6c9cb21b · outbound

This paper cites Medical SAM 2: Segment medical images as video via Segment Anything Model 2.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Medical SAM 2: Segment medical images as video via Segment Anything Model 2

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T23:24:39.193018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation add18804-9cd4-4c99-a062-9b4147aabd05 · outbound

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

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T23:24:39.197692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d68aadc-434b-40a8-9e32-d3e79b37fc77 · outbound

This paper cites Segment anything model for medical image analysis: an experimental study,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Segment anything model for medical image analysis: an experimental study,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.638651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 731a20c1-1f67-4d74-b58b-9790c5463de4 · outbound

This paper cites Test-Time Training with Masked Autoencoders.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Test-Time Training with Masked Autoencoders

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T23:24:39.205973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dbf8133-18aa-42da-b3fe-d63e4214a679 · outbound

This paper cites Ttt++: When does self-supervised test-time training fail or thrive?.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Ttt++: When does self-supervised test-time training fail or thrive?

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.627521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cdb177d4-a123-47dc-a797-77a3ac369dfd · outbound

This paper cites Depth- aware test-time training for zero-shot video object segmentation,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Depth- aware test-time training for zero-shot video object segmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.615825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 64cf7667-eb03-4a7e-831d-d84a90c1028c · outbound

This paper cites Test-time adaptable neural networks for robust medical image segmentation,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Test-time adaptable neural networks for robust medical image segmentation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.603798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 540cdd80-aa94-40a0-bdcc-98bb3e52f729 · outbound

This paper cites Test- time training with self-supervision for generalization under distribution shifts,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Test- time training with self-supervision for generalization under distribution shifts,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.590825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a3477aa8-b805-4aa1-8f1a-eee301e6885c · outbound

This paper cites Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T23:24:39.225259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9ae7491-9066-4ca0-9a70-3cbaf353dafe · outbound

This paper cites Segment Anything in Medical Images and Videos: Benchmark and Deployment.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Segment Anything in Medical Images and Videos: Benchmark and Deployment

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T23:24:39.229349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16000dbb-c1ca-44dc-8c5f-0cab6f29365b · outbound

This paper cites Automated bolus detection in videofluoroscopic images of swallowing using mask- rcnn,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Automated bolus detection in videofluoroscopic images of swallowing using mask- rcnn,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.577223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2c4c9ff7-bcb0-48af-9904-0e09f3b74fe7 · outbound

This paper cites Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: Killing two birds with one stone?.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: Killing two birds with one stone?

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.565126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 76a1d375-3292-434e-a7e7-55d90d648c24 · outbound

This paper cites Deep learning-based auto-segmentation and evaluation of vallecular residue in videofluo- roscopy,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Deep learning-based auto-segmentation and evaluation of vallecular residue in videofluo- roscopy,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.549552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bef1fe51-8c0b-4b24-9a14-c4c5965ab5a0 · outbound

This paper cites Peci-net: Bolus segmentation from video fluo- roscopic swallowing study images using preprocessing ensemble and cascaded inference,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Peci-net: Bolus segmentation from video fluo- roscopic swallowing study images using preprocessing ensemble and cascaded inference,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.535728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a7835b5c-9eea-4e6c-bffe-d1d98d66f299 · outbound

This paper cites Tent: Fully test-time adaptation by entropy minimization,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Tent: Fully test-time adaptation by entropy minimization,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.522080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d4e2be78-9bb5-4b00-ac82-d884578b369c · outbound

This paper cites PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-09T23:24:39.370591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f51cb633-6a93-464f-9059-e1fe1e34aa0f · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T23:24:39.253199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 738d24a3-b7d6-4660-bb2a-f79851a21e43 · outbound

This paper cites Stoyanov, Z.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Stoyanov, Z

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.508459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8fbc0f03-1c9c-45c6-83dd-20f4799f7a0d · outbound

This paper cites Road extraction by deep residual u-net,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Road extraction by deep residual u-net,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.496018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 71ad958e-00a5-45f6-8bfe-10461a72f5e3 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Attention U-Net: Learning Where to Look for the Pancreas

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T23:24:39.264557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2faafe61-6a50-4d63-97a8-757bbf05f73a · outbound

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

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T23:24:39.268767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c717c1c6-a18d-4b44-bb5f-5d47ba67dd75 · outbound

This paper cites Video-transunet: Temporally blended vision transformer for ct vfss instance segmentation,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Video-transunet: Temporally blended vision transformer for ct vfss instance segmentation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.484168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 16951f5b-2356-4322-8425-a1d21a985fdb · outbound

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

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T23:24:39.276143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf1f5f9d-647a-43bc-9ed9-eec991725a9a · outbound

This paper cites Video-swinunet: Spatio-temporal deep learning frame- work for vfss instance segmentation,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Video-swinunet: Spatio-temporal deep learning frame- work for vfss instance segmentation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.472995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0af3ca3f-1725-4237-bbc2-45abe3a89fbf · outbound

This paper cites Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation,.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:24:39.461177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.