Paper Citation Record · LEDGER

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation

As of 21 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2501.02788.

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

pith.paper-citation-record.v1
2501.02788 v2

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:08:30.084177Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

21 of 21 outbound references displayed

verified exact1
verified fuzzy16
unresolved4
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8169fb43-e1b6-4e18-a437-b86a5e2c44c1 · outbound

This paper cites Towards robust general medical image segmentation,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Towards robust general medical image segmentation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:31.480060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 565d0c57-3932-4a62-8847-b66ba1674b8b · outbound

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

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Medical image segmentation using deep learning: A survey,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:31.421818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1c6dcc99-eb9d-4254-bc20-195b651b15af · outbound

This paper cites Pet-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Pet-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:31.371467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bda48d9f-15ca-4f70-8868-284b58a8a72d · outbound

This paper cites An integrated visualization system for surgical planning and guidance using image fusion and an open mr,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation An integrated visualization system for surgical planning and guidance using image fusion and an open mr,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:31.317493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fe66a24d-b5fb-46c0-8f95-d298a09fe24a · outbound

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

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation U-net: Con- volutional networks for biomedical image segmentation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:31.221426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 220ad115-d3c8-4542-9aea-5fff59fb9cc3 · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmentation,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Unet++: A nested u-net architecture for medical image segmentation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:31.195775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aeab89e3-dba0-482c-8c8a-d909b4eb12d4 · outbound

This paper cites Unet 3+: A full-scale connected unet for medical image segmentation,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Unet 3+: A full-scale connected unet for medical image segmentation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:31.022990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f5b3d263-6283-43e7-b133-b19722e10295 · outbound

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

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T22:08:29.597235Z

Source-reported events for the cited work

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Observation 8ebe6291-b785-4685-b5dc-9a9ed70cd66c · outbound

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

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T22:08:29.645558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1c3a9a8-f047-4d4a-b43f-ca92608dd371 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T22:08:29.682780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 240d00e8-1424-44ce-ae7d-63361f226dd2 · outbound

This paper cites Mixed transformer u-net for medical image segmentation,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Mixed transformer u-net for medical image segmentation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:30.916722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eeb0ac45-ca5a-42d8-8cff-4eb3fecd442e · outbound

This paper cites Optimizing vision trans- formers for medical image segmentation,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Optimizing vision trans- formers for medical image segmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:30.902274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 87d58304-0069-470b-9b54-ccd6823402d3 · outbound

This paper cites Introduction to radiomics,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Introduction to radiomics,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:30.787724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 84312217-ddf9-45eb-8dd2-e993dc61138a · outbound

This paper cites Standardised convolutional filtering for radiomics.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Standardised convolutional filtering for radiomics

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:08:30.239070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8adc5c52-3571-4bb3-b351-7c31ffaa0fe3 · outbound

This paper cites Interpretation of radiomics features–a pictorial review,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Interpretation of radiomics features–a pictorial review,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:30.774435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e27552ae-5279-4dcb-95de-b3b1fcd1f4bf · outbound

This paper cites In search of a general picture processing operator,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation In search of a general picture processing operator,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:30.635465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cd127d39-ec7f-446e-b05b-af844e3f3e56 · outbound

This paper cites Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:30.547526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8aac0e54-8ede-416d-a31b-fdae6c52b7fc · outbound

This paper cites Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:30.444321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5a29a63b-e2a5-4791-bb72-5ff74f2628bf · outbound

This paper cites Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:30.430001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9b414f4c-c69f-4a4a-9f84-ca00fb30bb30 · outbound

This paper cites Comparing images using the hausdorff distance,.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Comparing images using the hausdorff distance,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:08:30.355036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 79ca86a8-4cc0-4aad-9e94-222f3a5e4c5f · outbound

This paper cites Decoupled Weight Decay Regularization.

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation Decoupled Weight Decay Regularization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T22:08:30.084177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.