Paper Citation Record · LEDGER

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation

As of 21 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2412.06088.

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

pith.paper-citation-record.v1
2412.06088 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:06:32.663052Z

measured 37 of 37 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

37 of 37 outbound references displayed

verified exact1
verified fuzzy15
unresolved21
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 845a1abb-b8b6-4f68-903a-c82de2bff6eb · outbound

This paper cites Segnext: Rethinking convolutional attention design for semantic segmentation,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Segnext: Rethinking convolutional attention design for semantic segmentation,

Reference 1

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Source-reported events for the cited work

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Observation 2f5c0421-28ea-45c0-9dd7-55a9428ce062 · outbound

This paper cites Densely connected convolutional networks,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Densely connected convolutional networks,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.547635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 860bc12c-393c-4d4b-a850-0e27f6a1cad0 · outbound

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

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation nnu- net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.903711Z

Source-reported events for the cited work

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Observation b63da550-5c64-49d0-834a-0188add09dcb · outbound

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

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.552685Z

Source-reported events for the cited work

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Observation 15849666-c3d9-4465-ab45-6af4aaf599c8 · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.555954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fce1765-b306-4a1e-9fd7-3b5ea5d9f1ca · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.559435Z

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Observation 5adbe83f-74c2-43be-be59-cee5147bb5dc · outbound

This paper cites Transattunet: Multi-level attention-guided u-net with transformer for medical image segmentation,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Transattunet: Multi-level attention-guided u-net with transformer for medical image segmentation,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.562765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a3fdab4-7a8b-4b32-ba07-88e35e3fc0b2 · outbound

This paper cites Bottleneck transformers for visual recognition,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Bottleneck transformers for visual recognition,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.883818Z

Source-reported events for the cited work

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Observation c0887c78-ae7c-460f-83c5-e680652f810a · outbound

This paper cites Squeeze-and-excitation networks,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Squeeze-and-excitation networks,

Reference 9

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Source-reported events for the cited work

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Observation cf471d29-5ed3-4742-9ece-46d5b76edaa8 · outbound

This paper cites Fcanet: Frequency channel attention networks,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Fcanet: Frequency channel attention networks,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.571937Z

Source-reported events for the cited work

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Observation 7765cc2f-2c0e-4bc6-bbd2-ba39742e700f · outbound

This paper cites Cbam: Convolutional block attention module,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Cbam: Convolutional block attention module,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.574866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1c42aa3-3b5f-4f5c-8a59-e3d81f15d00a · outbound

This paper cites A real-time algorithm for signal analysis with the help of the wavelet transform,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation A real-time algorithm for signal analysis with the help of the wavelet transform,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.864401Z

Source-reported events for the cited work

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Observation 0e27e65b-6af8-4430-a370-385ca3007c1e · outbound

This paper cites Object detection with discriminatively trained part-based models,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Object detection with discriminatively trained part-based models,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.584015Z

Source-reported events for the cited work

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Observation 6970e8c9-696a-457d-8013-e62b4baa35b6 · outbound

This paper cites Spatial transformer networks,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Spatial transformer networks,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.586984Z

Source-reported events for the cited work

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Observation 70ee9cf3-598a-42c5-b095-fecd7f1a91a0 · outbound

This paper cites Deformable convolutional networks,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Deformable convolutional networks,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.589898Z

Source-reported events for the cited work

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Observation a840374d-120f-4276-b1f4-05d8a24eec03 · outbound

This paper cites Multi-Scale Context Aggregation by Dilated Convolutions.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Multi-Scale Context Aggregation by Dilated Convolutions

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.592758Z

Source-reported events for the cited work

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Observation a40eafd8-5dc7-4617-8f04-ef2eae327799 · outbound

This paper cites Spatial pyramid pooling in deep convolutional networks for visual recognition,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Spatial pyramid pooling in deep convolutional networks for visual recognition,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.595562Z

Source-reported events for the cited work

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Observation b70b0751-1412-479b-8ce0-aab8e1e36619 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.598452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4b8873f-7906-45df-9639-29abcd5bb1b2 · outbound

This paper cites Crossvit: Cross-attention multi- scale vision transformer for image classification,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Crossvit: Cross-attention multi- scale vision transformer for image classification,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.601397Z

Source-reported events for the cited work

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Observation 6def2034-b00e-4c74-842e-2a6fa048ac30 · outbound

This paper cites Multiscale vision transformers,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Multiscale vision transformers,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.833037Z

Source-reported events for the cited work

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Observation 472f47f6-773f-448c-b8e9-94d8384a003d · outbound

This paper cites Beyond self-attention: Deformable large kernel attention for medical image segmentation,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Beyond self-attention: Deformable large kernel attention for medical image segmentation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.826230Z

Source-reported events for the cited work

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Observation 66c688fa-0581-4701-957c-6c9b14ffa56c · outbound

This paper cites Visual attention network,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Visual attention network,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.610224Z

Source-reported events for the cited work

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Observation d9586a34-cba5-422c-802e-bbe1bc920451 · outbound

This paper cites Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.814851Z

Source-reported events for the cited work

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Observation 34c13775-2d43-4a3d-a23e-1aa690d226b1 · outbound

This paper cites Segmentation of glioma tumors in brain using deep convolutional neural network,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Segmentation of glioma tumors in brain using deep convolutional neural network,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.805893Z

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 5a496a07-2004-4d32-903d-8284e539d0f1 · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 26

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Source-reported events for the cited work

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Observation 12fd113a-08a3-4a9f-a8af-581f3e05ecad · outbound

This paper cites Transdeeplab: Convolution-free transformer- based deeplab v3+ for medical image segmentation,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Transdeeplab: Convolution-free transformer- based deeplab v3+ for medical image segmentation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.797766Z

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 40b046be-6bcd-4fd3-92c6-d14fdca84e24 · outbound

This paper cites OrthoNets: Orthogonal Channel Attention Networks.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation OrthoNets: Orthogonal Channel Attention Networks

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:06:32.697913Z

Source-reported events for the cited work

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Observation 87a18f78-1faf-4faf-99a1-c200eee89550 · outbound

This paper cites Disan: Direc- tional self-attention network for rnn/cnn-free language understanding,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Disan: Direc- tional self-attention network for rnn/cnn-free language understanding,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.789125Z

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 1a99d4bd-d3ba-4e29-9ad9-ebb33d15b5b5 · outbound

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

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 30

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Observation d69d88ba-773f-4390-8e2a-114312022192 · outbound

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

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Swin-unet: Unet-like pure transformer for medical image segmenta- tion,

Reference 31

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Observation 9655b520-1816-4c15-8e35-a29164cc59d6 · outbound

This paper cites Resunet+: A new convolutional and attention block-based approach for brain tumor segmentation,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Resunet+: A new convolutional and attention block-based approach for brain tumor segmentation,

Reference 32

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Observation e8548234-85c5-4853-a4cc-cc2a38cba29f · outbound

This paper cites Two-stage cascaded u- net: 1st place solution to brats challenge 2019 segmentation task,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Two-stage cascaded u- net: 1st place solution to brats challenge 2019 segmentation task,

Reference 33

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Observation cf07816c-4f22-40a0-8a13-ffc92ccb65de · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Unetr: Transformers for 3d medical image segmentation,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:32.641280Z

Source-reported events for the cited work

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Observation 805770eb-d4b2-47ed-aaaa-e510e5b52e75 · outbound

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

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.754522Z

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 c5b228d1-025e-4b1d-a9a4-582b60b7b106 · outbound

This paper cites Redundancy reduction in semantic segmentation of 3d brain tumor mris,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Redundancy reduction in semantic segmentation of 3d brain tumor mris,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.745304Z

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 8f167609-015a-4de5-91fd-dadfe1e5c142 · outbound

This paper cites Optimized u- net for brain tumor segmentation,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Optimized u- net for brain tumor segmentation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.736637Z

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 fb80bfa7-325e-4017-a8ed-05ee36cd435d · outbound

This paper cites Coupling nnu-nets with expert knowledge for accurate brain tumor segmentation from mri,.

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation Coupling nnu-nets with expert knowledge for accurate brain tumor segmentation from mri,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:06:32.727580Z

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.

Pith citing papers

No inbound Pith citation observations are available.