Paper Citation Record · LEDGER

Gland Segmentation Using SAM With Cancer Grade as a Prompt

As of 16 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2501.14718.

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

pith.paper-citation-record.v1
2501.14718 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:54:48.839405Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:54:48.721150Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T14:54:48.924911Z

Reference resolution

25 of 25 outbound references displayed

verified exact0
verified fuzzy18
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bb3f1604-2e74-4d19-b36f-a5eed9d54e56 · outbound

This paper cites A large proportion of col- orectal cancers are classified as adenocarcinomas [1].

Gland Segmentation Using SAM With Cancer Grade as a Prompt A large proportion of col- orectal cancers are classified as adenocarcinomas [1]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.353008Z

Source-reported events for the cited work

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

Observation 235ac036-4380-4660-b7d4-f2f6d91a7eb4 · outbound

This paper cites Gland Segmentation Using SAM With Cancer Grade as a Prompt.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Gland Segmentation Using SAM With Cancer Grade as a Prompt

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T14:54:48.943132Z

Source-reported events for the cited work

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

Observation 233ed8a9-a6d9-4476-82d8-d55001db6e28 · outbound

This paper cites Data Preparation The open-source Gland Segmentation Challenge (GlaS) [15] dataset was used in this study.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Data Preparation The open-source Gland Segmentation Challenge (GlaS) [15] dataset was used in this study

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.319564Z

Source-reported events for the cited work

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

Observation b4e0385c-b66c-4b7f-92de-7ae2aafd4e5b · outbound

This paper cites A new method to provide grade prompt is designed.

Gland Segmentation Using SAM With Cancer Grade as a Prompt A new method to provide grade prompt is designed

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.302234Z

Source-reported events for the cited work

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

Observation 0c60e623-6630-43e5-9889-c3f135d03b2d · outbound

This paper cites Primar- ily trained on a broad dataset, it demonstrates impressive zero-shot performance involving natural images.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Primar- ily trained on a broad dataset, it demonstrates impressive zero-shot performance involving natural images

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.335868Z

Source-reported events for the cited work

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

Observation 1a3a5340-842b-4d9b-98e6-2b269ded934a · outbound

This paper cites an unresolved cited work.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:54:49.281241Z

Source-reported events for the cited work

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

Observation 76975470-4bbb-4844-a22f-bcbd69f44697 · outbound

This paper cites an unresolved cited work.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:54:49.259685Z

Source-reported events for the cited work

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

Observation bd81c330-f9e8-484f-890e-1b1fb63613ca · outbound

This paper cites General insight into cancer: An overview of colorectal cancer,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt General insight into cancer: An overview of colorectal cancer,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.242637Z

Source-reported events for the cited work

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

Observation 5b07be12-d274-4587-9597-1762a6f57063 · outbound

This paper cites Colorectal carcinoma: Pathologic aspects,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Colorectal carcinoma: Pathologic aspects,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T14:54:48.756503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34881f9c-26ad-45df-8f2e-d2876050afd5 · outbound

This paper cites Mild-net: Minimal information loss dilated network for gland instance segmentation in colon histology images,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Mild-net: Minimal information loss dilated network for gland instance segmentation in colon histology images,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.207361Z

Source-reported events for the cited work

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

Observation 76b92fd1-baae-431d-b809-5f75db6240a4 · outbound

This paper cites Gcsba-net: Gabor-based and cascade squeeze bi- attention network for gland segmentation,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Gcsba-net: Gabor-based and cascade squeeze bi- attention network for gland segmentation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.191628Z

Source-reported events for the cited work

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

Observation cdbaf09d-66cf-40ac-a759-2d586e25d00c · outbound

This paper cites Segment anything,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Segment anything,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.169834Z

Source-reported events for the cited work

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

Observation 494d2767-e8c1-4fba-ba6d-e8f351229481 · outbound

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

Gland Segmentation Using SAM With Cancer Grade as a Prompt Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T14:54:48.778617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d353df9-92dd-4231-a6fa-dc27c66e7cd8 · outbound

This paper cites Segment anything in medical images,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Segment anything in medical images,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.149544Z

Source-reported events for the cited work

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

Observation 3b583281-3360-476f-93d9-306a37bd5d08 · outbound

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

Gland Segmentation Using SAM With Cancer Grade as a Prompt Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T14:54:48.789080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaccfe43-74ef-4749-9d19-4381ea0bc6d4 · outbound

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

Gland Segmentation Using SAM With Cancer Grade as a Prompt Segment anything model for medical image analysis: an experimental study,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.127149Z

Source-reported events for the cited work

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

Observation f4c0c817-adf6-4f0f-b132-1068333e07d7 · outbound

This paper cites Glandular morphometrics for objective grading of colorectal adenocarcinoma histol- ogy images,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Glandular morphometrics for objective grading of colorectal adenocarcinoma histol- ogy images,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.107294Z

Source-reported events for the cited work

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

Observation a791ef55-2f8d-4851-873f-b2c265963e64 · outbound

This paper cites Cgs-net: Classification-guided segmentation network for improved gland segmenta- tion,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Cgs-net: Classification-guided segmentation network for improved gland segmenta- tion,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.089022Z

Source-reported events for the cited work

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

Observation 768866f3-724b-4fee-95a2-d0d924e980dc · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.070775Z

Source-reported events for the cited work

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

Observation 9b1f723b-f635-48ae-8ee3-981230ddb149 · outbound

This paper cites Grad- cam++: Generalized gradient-based visual explanations for deep convolutional networks,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Grad- cam++: Generalized gradient-based visual explanations for deep convolutional networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.053545Z

Source-reported events for the cited work

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

Observation 90ac0cf3-20b0-499a-ad68-81660151e39d · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt U-net: Convolutional networks for biomedical image segmentation,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T14:54:48.819443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4063ea8f-5f22-4ffe-83c0-1416d3025e19 · outbound

This paper cites Gland segmentation in colon histology images: The glas challenge contest,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Gland segmentation in colon histology images: The glas challenge contest,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.022823Z

Source-reported events for the cited work

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

Observation 69446530-9acc-41d2-ab9a-0370eca90f38 · outbound

This paper cites Training data-efficient image transformers & distillation through attention,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Training data-efficient image transformers & distillation through attention,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:49.005967Z

Source-reported events for the cited work

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

Observation 9ea57c4c-1ce0-4c35-b2d2-582515ab81de · outbound

This paper cites Hi- gmisnet: generalized medical image segmentation us- ing dwt based multilayer fusion and dual mode atten- tion into high resolution pgan,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Hi- gmisnet: generalized medical image segmentation us- ing dwt based multilayer fusion and dual mode atten- tion into high resolution pgan,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:48.985255Z

Source-reported events for the cited work

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

Observation c599b682-c097-4637-9cc0-7651b0e26ece · outbound

This paper cites Pannuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification,.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Pannuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:54:48.964080Z

Source-reported events for the cited work

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

Pith citing papers

Observation 235ac036-4380-4660-b7d4-f2f6d91a7eb4 · inbound