Paper Citation Record · LEDGER

Adapting a Segmentation Foundation Model for Medical Image Classification

As of 18 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2505.06217.

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

pith.paper-citation-record.v1
2505.06217 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:48:34.012079Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

25 of 25 outbound references displayed

verified exact1
verified fuzzy12
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 444e6378-f8d7-4fec-b946-6476153ada6f · outbound

This paper cites Deep residual learning for image recognition,.

Adapting a Segmentation Foundation Model for Medical Image Classification Deep residual learning for image recognition,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:34.837454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5721dfa1-c840-4b5a-a0e2-15155d17e89c · outbound

This paper cites Squeeze-and-excitation networks,.

Adapting a Segmentation Foundation Model for Medical Image Classification Squeeze-and-excitation networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:34.824737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 152cc086-cf19-437f-9248-3194caa7a169 · outbound

This paper cites Swin Transformer v2: Scaling up capacity and resolution,.

Adapting a Segmentation Foundation Model for Medical Image Classification Swin Transformer v2: Scaling up capacity and resolution,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:34.753288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c64fc524-1aad-4c28-b0b1-ffcc3f3c4e94 · outbound

This paper cites Segment Anything.

Adapting a Segmentation Foundation Model for Medical Image Classification Segment Anything

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:33.702212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9be013f2-4bbd-4351-8220-c0a39bafa4bb · outbound

This paper cites Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging.

Adapting a Segmentation Foundation Model for Medical Image Classification Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:33.708954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f910742f-4d00-4ea4-93a1-a399adae548d · outbound

This paper cites Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelines.

Adapting a Segmentation Foundation Model for Medical Image Classification Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelines

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:48:34.216569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cc44000b-a057-4c4a-96f6-3aee34822848 · outbound

This paper cites Segment Anything Model for Medical Images?.

Adapting a Segmentation Foundation Model for Medical Image Classification Segment Anything Model for Medical Images?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:33.726287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4e1277a-248c-470c-a174-7d778cc6bea2 · outbound

This paper cites Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model.

Adapting a Segmentation Foundation Model for Medical Image Classification Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:33.732535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 386945aa-b216-4f34-916e-35b1cde41682 · outbound

This paper cites SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation.

Adapting a Segmentation Foundation Model for Medical Image Classification SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:33.738376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96655965-45ea-4002-b68a-512cff95fdac · outbound

This paper cites Segment Anything in Medical Images.

Adapting a Segmentation Foundation Model for Medical Image Classification Segment Anything in Medical Images

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:33.743359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ec0c37d-a0cd-48df-b750-9f37a49bb1ef · outbound

This paper cites Polyp-SAM: Transfer SAM for Polyp Segmentation.

Adapting a Segmentation Foundation Model for Medical Image Classification Polyp-SAM: Transfer SAM for Polyp Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:33.813000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef809da6-db73-4f4f-a354-f05546c71f00 · outbound

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

Adapting a Segmentation Foundation Model for Medical Image Classification Customized Segment Anything Model for Medical Image Segmentation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:33.850511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a5c5bb3-1f8e-4525-9a6a-b45839f47e6e · outbound

This paper cites Boosting medical image classification with segmentation foundation model,.

Adapting a Segmentation Foundation Model for Medical Image Classification Boosting medical image classification with segmentation foundation model,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:34.708309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 64102d11-884e-488f-be94-c25c778491ad · outbound

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

Adapting a Segmentation Foundation Model for Medical Image Classification An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:33.861283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6b3369d-a44a-43e8-9196-19b1fb69532b · outbound

This paper cites Masked autoencoders are scalable vision learners,.

Adapting a Segmentation Foundation Model for Medical Image Classification Masked autoencoders are scalable vision learners,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:34.694031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation baa7d89b-c8f1-4a6b-853a-110e66bc306f · outbound

This paper cites Data-driven deep supervision for skin lesion classification,.

Adapting a Segmentation Foundation Model for Medical Image Classification Data-driven deep supervision for skin lesion classification,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:34.668482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a55c8b35-5f6d-417b-a49f-61640afff8ac · outbound

This paper cites IHCSurv: Effective immunohistochemistry priors for cancer survival analysis in gigapixel multi-stain whole slide images,.

Adapting a Segmentation Foundation Model for Medical Image Classification IHCSurv: Effective immunohistochemistry priors for cancer survival analysis in gigapixel multi-stain whole slide images,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:34.572368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ac0d117-d2fd-4662-b69e-15ef8dc8c182 · outbound

This paper cites InTracker: An integrated detector-tracker framework for cell detection and tracking,.

Adapting a Segmentation Foundation Model for Medical Image Classification InTracker: An integrated detector-tracker framework for cell detection and tracking,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:34.557259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c7d1750a-8f2f-4209-8ec2-61efe83eb151 · outbound

This paper cites Path- GPTOmic: A balanced multi-modal learning framework for survival outcome prediction,.

Adapting a Segmentation Foundation Model for Medical Image Classification Path- GPTOmic: A balanced multi-modal learning framework for survival outcome prediction,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:34.500403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d81da9ea-5f3c-48a6-901c-240b6b2f9b24 · outbound

This paper cites ECA-Net: Efficient channel attention for deep convolutional neural networks,.

Adapting a Segmentation Foundation Model for Medical Image Classification ECA-Net: Efficient channel attention for deep convolutional neural networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:34.421386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b95bf4c1-7e0a-401a-92c9-1ef004d91bfa · outbound

This paper cites MedMNIST classification decathlon: A lightweight autoML benchmark for medical image analysis,.

Adapting a Segmentation Foundation Model for Medical Image Classification MedMNIST classification decathlon: A lightweight autoML benchmark for medical image analysis,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:34.403611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fb032761-503a-4780-9b08-91cb95e8d828 · outbound

This paper cites The 2nd diabetic retinopathy – grading and image quality estimation challenge,.

Adapting a Segmentation Foundation Model for Medical Image Classification The 2nd diabetic retinopathy – grading and image quality estimation challenge,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:34.363678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7af1bdc1-d53e-4534-87c7-5916406d1f18 · outbound

This paper cites Dataset of breast ultrasound images,.

Adapting a Segmentation Foundation Model for Medical Image Classification Dataset of breast ultrasound images,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:34.002737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e22f6a75-a653-4c01-841f-81c45078cd1e · outbound

This paper cites an unresolved cited work.

Adapting a Segmentation Foundation Model for Medical Image Classification Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:48:34.264616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e4cab500-6649-4084-822d-457ba52b0213 · outbound

This paper cites Decoupled Weight Decay Regularization.

Adapting a Segmentation Foundation Model for Medical Image Classification Decoupled Weight Decay Regularization

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:34.012079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.