Paper Citation Record · LEDGER

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation

As of 20 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2502.00314.

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

pith.paper-citation-record.v1
2502.00314 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:29:38.520327Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

34 of 34 outbound references displayed

verified exact2
verified fuzzy19
unresolved13
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 30a5ab53-3fab-4517-a368-7e2ef5b0715f · outbound

This paper cites Medical Image Segmentation Review: The success of U-Net.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Medical Image Segmentation Review: The success of U-Net

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b1f07f2-0028-4618-9f1d-b13b39c0e1b7 · outbound

This paper cites Advances in medical image segmentation: A comprehensive review of traditional, deep learning and hybrid approaches,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Advances in medical image segmentation: A comprehensive review of traditional, deep learning and hybrid approaches,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.843498Z

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Observation 30e6f8e4-8ef4-4783-bf7d-a720061ae9ea · outbound

This paper cites Deep learning for medical image segmentation: State-of-the-art advancements and challenges,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Deep learning for medical image segmentation: State-of-the-art advancements and challenges,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.834399Z

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Observation 1da33e4f-6a39-48df-a3ec-9046e840233e · outbound

This paper cites Brain tumor segmentation of mri images: A comprehensive review on the application of artificial intelligence tools,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Brain tumor segmentation of mri images: A comprehensive review on the application of artificial intelligence tools,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.825606Z

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Observation 69f36e9b-414e-457a-9170-77d86b430b1d · outbound

This paper cites Deep learning based brain tumor segmentation: a survey,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Deep learning based brain tumor segmentation: a survey,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.816571Z

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Observation bd06bbc4-a9e8-4ec7-bd6b-9722ca0b0591 · outbound

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

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation U-net: Convolutional networks for biomedical image segmen- tation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.805937Z

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Observation 51335b41-9c4b-44e3-b7ca-4e08399344fe · outbound

This paper cites The liver tumor segmentation benchmark (lits),.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation The liver tumor segmentation benchmark (lits),

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.796851Z

Source-reported events for the cited work

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Observation 894eba8d-0c30-42b2-9ee2-ec9154c22235 · outbound

This paper cites A survey on u-shaped networks in medical image segmentations,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation A survey on u-shaped networks in medical image segmentations,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.788428Z

Source-reported events for the cited work

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Observation c7f363ec-e5df-4fcf-b32e-beac31af3472 · outbound

This paper cites Attention u-net: Learning where to look for the pancreas,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Attention u-net: Learning where to look for the pancreas,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.780135Z

Source-reported events for the cited work

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Observation 67023a41-935a-4b64-978e-9b744fce7906 · outbound

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

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Unet++: A nested u-net architecture for medical image segmentation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.771851Z

Source-reported events for the cited work

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Observation e0ddc8c6-6873-43a6-a4ec-53a23ca6c67e · outbound

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

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Unet 3+: A full-scale connected unet for medical image segmentation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.762395Z

Source-reported events for the cited work

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Observation 665c0081-4d8f-4eca-ba9f-316e40ecbc45 · outbound

This paper cites H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.753585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d59fba6e-cd99-403b-8467-c3c4988e0776 · outbound

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

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.744314Z

Source-reported events for the cited work

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Observation 831fdc30-80a4-4973-a268-0f1c72827893 · outbound

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

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 14

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Source-reported events for the cited work

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Observation a47ced88-ad79-4ed2-beb1-254461d9785a · outbound

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

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.734999Z

Source-reported events for the cited work

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Observation 72b18977-09ff-4740-ab9a-ab1063cbadd6 · outbound

This paper cites Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.725501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 52b08391-7b45-4e4c-a58e-99c7607eec33 · outbound

This paper cites Laplacian-former: Overcoming the limitations of vision transformers in local texture detection,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Laplacian-former: Overcoming the limitations of vision transformers in local texture detection,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.715045Z

Source-reported events for the cited work

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Observation b02bf61a-00f3-41fd-b786-64906e63aa23 · outbound

This paper cites Msa2net: Multi-scale adaptive attention-guided network for medical image segmentation,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Msa2net: Multi-scale adaptive attention-guided network for medical image segmentation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.705877Z

Source-reported events for the cited work

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Observation 3086d52c-1408-4685-ad77-d53afd9bc774 · outbound

This paper cites Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights

Reference 19

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Source-reported events for the cited work

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Observation fd95baef-8cd3-459d-be7b-3897589c3cf6 · outbound

This paper cites A Survey of Mamba.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation A Survey of Mamba

Reference 20

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Source-reported events for the cited work

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Observation bf2ef26b-aadc-43c4-98e2-6756ffb601c1 · outbound

This paper cites Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis

Reference 21

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Observation aa6c778f-45d7-430e-b354-06ecc3872ea8 · outbound

This paper cites State Space Model for New-Generation Network Alternative to Transformers: A Survey.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation State Space Model for New-Generation Network Alternative to Transformers: A Survey

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T19:29:38.479232Z

Source-reported events for the cited work

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Observation f22db9cb-100b-4e1c-83ff-ee7300cf2b15 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 23

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Observation efcdd1c5-d2f3-44bb-ae27-790ffde4b348 · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 24

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Observation a2e14b20-9a44-4790-af92-b4e53931a20c · outbound

This paper cites LocalMamba: Visual State Space Model with Windowed Selective Scan.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation LocalMamba: Visual State Space Model with Windowed Selective Scan

Reference 25

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Observation 5eaf0ca0-0f68-4f2c-aa32-0dd9a2eb0478 · outbound

This paper cites PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition

Reference 26

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Observation 1c249157-9274-466d-8c69-77ab5f4085d2 · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T19:29:38.497048Z

Source-reported events for the cited work

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Observation 6085abb6-8544-4061-b49e-a21634ea319b · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation xLSTM: Extended Long Short-Term Memory

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T19:29:38.501312Z

Source-reported events for the cited work

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Observation 3aca4325-6e42-4549-87fb-54228353bcdd · outbound

This paper cites Vision-LSTM: xLSTM as Generic Vision Backbone.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Vision-LSTM: xLSTM as Generic Vision Backbone

Reference 29

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Source-reported events for the cited work

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Observation 01755911-b2db-4003-bf0b-d60085ef9073 · outbound

This paper cites xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart

Reference 30

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Observation 0a3c34d2-a6ef-475d-9042-c439caa63b79 · outbound

This paper cites Attention is all you need,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Attention is all you need,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.695986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 77d66e67-cc67-42cf-b7d3-cdca098046d8 · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.686018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c883f508-25e1-44d5-a8db-6e56d3ea076e · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:29:38.676671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fdd88eeb-9391-4135-bba0-ed2deb8a1432 · outbound

This paper cites Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge.

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-09T19:29:38.520327Z

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Unavailable: canonical work link unavailable.

Pith citing papers

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