Paper Citation Record · LEDGER

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation

As of 16 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2508.04058.

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

pith.paper-citation-record.v1
2508.04058 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T01:01:34.953851Z

measured 38 of 38 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 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

38 of 38 outbound references displayed

verified exact0
verified fuzzy26
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d2816d1f-c771-4ec0-b717-6f827e19e1ae · outbound

This paper cites Advances in medical image analysis with vision transformers: a comprehensive review.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Advances in medical image analysis with vision transformers: a comprehensive review

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:40.963865Z

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 27a489cb-a633-4fd6-8554-b0fbe27dd155 · outbound

This paper cites Token merging: Your vit but faster, in: ICLR.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Token merging: Your vit but faster, in: ICLR

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:40.717611Z

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 01170180-cf82-4182-abef-c5450d2f2913 · outbound

This paper cites an unresolved cited work.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T01:01:40.534903Z

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 1c8c3d9e-ce7b-479c-8a16-b6408d9decd2 · outbound

This paper cites Ghostvit: Expediting vision transformers via cheap operations.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Ghostvit: Expediting vision transformers via cheap operations

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:40.357703Z

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 e3195594-d3b2-4b59-b095-c02b62e0d8a6 · outbound

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

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation,in:Europeanconferenceoncomputervision,Springer

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:40.186445Z

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 1106bb2f-c2b5-4c78-9f9f-cfc811e84782 · outbound

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

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T01:01:31.942082Z

Source-reported events for the cited work

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Observation 5eb4b576-b2e2-4522-9fd4-f0cc8f61c7d6 · outbound

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

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T01:01:32.053456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d19fe9d-f782-4d22-bcfb-787a2e252924 · outbound

This paper cites Cpfnet:Contextpyramidfusionnetworkfor medical image segmentation.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Cpfnet:Contextpyramidfusionnetworkfor medical image segmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:39.994119Z

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 e2beda7c-2b47-4864-8e09-54d4a4c49f95 · outbound

This paper cites Ce-net: Context encoder network for 2d medical image segmentation.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Ce-net: Context encoder network for 2d medical image segmentation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:39.811083Z

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 3992b2fa-013c-4d4b-ab80-60869af57fac · outbound

This paper cites 6202–6212.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation 6202–6212

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:39.623297Z

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 67cd342b-8350-422d-98c7-23c6d9af6d70 · outbound

This paper cites Unet 3+: A full-scale connected unet for medical image segmentation, in: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Unet 3+: A full-scale connected unet for medical image segmentation, in: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:39.430867Z

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 0cba88b9-aa4a-4bec-a85e-4c8cadb75361 · outbound

This paper cites Missformer: An effective transformer for 2d medical image segmentation.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Missformer: An effective transformer for 2d medical image segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:39.284241Z

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 f489fbbb-07d5-4323-935f-d7bfe7dec276 · outbound

This paper cites BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T01:01:32.660023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd06b486-b53d-4cd6-8253-e626fcf6235f · outbound

This paper cites Dmsa-unet: Dual multi-scale attention makes unet more strong for medical image segmentation.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Dmsa-unet: Dual multi-scale attention makes unet more strong for medical image segmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:39.167775Z

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 812fb1b1-6caa-4bcb-a074-cbe6929cb604 · outbound

This paper cites Maxformer: Enhanced transformer for medical image segmentation with multi-attention and multi-scale features fusion.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Maxformer: Enhanced transformer for medical image segmentation with multi-attention and multi-scale features fusion

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:39.011363Z

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 c91312c8-8e33-4aaf-82ad-195cdcb987ba · outbound

This paper cites Ds- transunet: Dual swin transformer u-net for medical image segmenta- tion.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Ds- transunet: Dual swin transformer u-net for medical image segmenta- tion

Reference 16

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T01:01:35.464018Z

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 30a3ac73-c25c-4e02-97e7-57ed0d511e65 · outbound

This paper cites Adavit: Adaptive vision transformers for efficient image recognition, in: Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, pp.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Adavit: Adaptive vision transformers for efficient image recognition, in: Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, pp

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:38.866254Z

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 0fdba78f-4688-4cfc-979a-8b799b6b886f · outbound

This paper cites an unresolved cited work.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-06T01:01:38.683001Z

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 d932b89e-00c3-43d5-a9a1-872cf600c2cd · outbound

This paper cites Dynam- icvit:Efficientvisiontransformerswithdynamictokensparsification.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Dynam- icvit:Efficientvisiontransformerswithdynamictokensparsification

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:38.521896Z

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 afa639e1-93c7-4b56-9da2-82d96b1d7980 · outbound

This paper cites an unresolved cited work.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-06T01:01:38.346782Z

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 e5e6b2ab-5fc8-4790-9158-980de6ffe96d · outbound

This paper cites Grad-cam:Visualexplanationsfromdeepnetworksvia gradient-basedlocalization,in:ProceedingsoftheIEEEinternational conference on computer vision, pp.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Grad-cam:Visualexplanationsfromdeepnetworksvia gradient-basedlocalization,in:ProceedingsoftheIEEEinternational conference on computer vision, pp

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:38.248163Z

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 96f8b064-8ca8-4655-bb36-06d9568d5370 · outbound

This paper cites Smanet: Superpixel-guided multi-scale attention network for medical image segmentation.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Smanet: Superpixel-guided multi-scale attention network for medical image segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:38.143047Z

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 5c98cd6b-8912-4455-b1bd-5158ce1a6b41 · outbound

This paper cites Msrf-net:amulti-scale residual fusion network for biomedical image segmentation.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Msrf-net:amulti-scale residual fusion network for biomedical image segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:38.020388Z

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 2ff04a2e-f085-423a-b6d1-8228cd472c10 · outbound

This paper cites Patch slimming for efficient vision transformers, in: Proceedings of theIEEE/CVFConferenceonComputerVisionandPatternRecogni- tion, pp.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Patch slimming for efficient vision transformers, in: Proceedings of theIEEE/CVFConferenceonComputerVisionandPatternRecogni- tion, pp

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:37.843540Z

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 3455211f-0c9b-4178-8eb4-7e0a31f425f1 · outbound

This paper cites Accelerating transformers with spectrum-preserving token merging.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Accelerating transformers with spectrum-preserving token merging

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:37.670291Z

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 af8feab8-cbbd-4d53-b3ee-54e112352a67 · outbound

This paper cites an unresolved cited work.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-06T01:01:37.503171Z

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 0217da97-6660-4a07-9e7b-6bbef9741336 · outbound

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

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Mixed transformer u-net for medical image segmentation, Z

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:37.339472Z

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 5b2d5c87-783e-4ebc-a2b1-20845b58427b · outbound

This paper cites Kvt: k-nn attention for boosting vision transformers, in: European conference on computer vision, Springer.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Kvt: k-nn attention for boosting vision transformers, in: European conference on computer vision, Springer

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:37.157243Z

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 24bb7219-c557-422a-a470-e12ab5a39131 · outbound

This paper cites Pvt v2: Improved baselines with pyramid vision transformer.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Pvt v2: Improved baselines with pyramid vision transformer

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:37.052202Z

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 b5a12197-c1fe-42aa-aabe-319703827ef2 · outbound

This paper cites an unresolved cited work.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-06T01:01:36.935101Z

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 c119c6a4-3378-4435-98a1-80c7ae3e76cb · outbound

This paper cites Dcsau-net: A deeper and more compact split-attention u-net for medical image segmentation.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Dcsau-net: A deeper and more compact split-attention u-net for medical image segmentation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:36.583060Z

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 875a9659-fc7a-46a5-806e-1d6a56bffb7b · outbound

This paper cites Msaanet: Multi-scale axial attention network for medical image segmentation, in: 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Msaanet: Multi-scale axial attention network for medical image segmentation, in: 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:36.461760Z

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 0c75022c-078e-4262-9f12-01992f808c0c · outbound

This paper cites an unresolved cited work.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-06T01:01:36.265442Z

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 06468dab-b77a-4066-80b9-6a6ef187bc09 · outbound

This paper cites Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T01:01:34.636339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de5884f2-dcb8-459a-8e6f-91842ab61f95 · outbound

This paper cites 2881–2890.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation 2881–2890

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:36.077674Z

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 56b5efa5-fb0f-497a-9796-91303e51e433 · outbound

This paper cites an unresolved cited work.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-06T01:01:35.883867Z

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 260ab125-5648-4d91-924b-504d5620988d · outbound

This paper cites Biformer: Vision transformer with bi-level routing attention, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recogni- tion, pp.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Biformer: Vision transformer with bi-level routing attention, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recogni- tion, pp

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:35.696450Z

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 6fa41690-c363-4992-8660-3b1235bb1d04 · outbound

This paper cites Advances in neural information processing systems 34, 12077–12090.

TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation Advances in neural information processing systems 34, 12077–12090

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T01:01:36.736632Z

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

No inbound Pith citation observations are available.