Paper Citation Record · LEDGER

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 inbound Pith citation observations for arXiv:2201.01266.

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

pith.paper-citation-record.v1
2201.01266 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 25 of 25 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 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:08:17.549091Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
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External citation measurements

32
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6173d31e-4aec-4d28-860e-cdaebe869494 · inbound

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge cites this paper.

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T14:14:48.336597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad6958ab-1095-4881-8601-f4f04f21be33 · inbound

Generalizable automated ischaemic stroke lesion segmentation with vision transformers cites this paper.

Generalizable automated ischaemic stroke lesion segmentation with vision transformers Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-08T14:22:18.740332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 068f0e2c-00c7-4109-b824-9a6933ce3cc4 · inbound

Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis cites this paper.

Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-16T12:08:17.549091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfc5afd6-330f-4ac1-bf3c-41c7c46ef457 · inbound

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI cites this paper.

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:46.213440Z

Source-reported events for the cited work

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Observation 7a9f4fe8-aa3d-46e2-b811-fba94c65a72d · inbound

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting cites this paper.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.956281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d64cb5de-cc4d-4f68-9c8f-03bda5bfe386 · inbound

F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement cites this paper.

F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:24:52.485452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06620db0-1f7f-4322-89f5-e33be5c98795 · inbound

Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement cites this paper.

Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T15:10:59.652996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 463a27ca-0888-4746-b7d4-7f2a0afe6025 · inbound

Differential-UMamba: Rethinking Tumor Segmentation Under Limited Data Scenarios cites this paper.

Differential-UMamba: Rethinking Tumor Segmentation Under Limited Data Scenarios Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-19T03:02:00.312933Z

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 22b74346-611d-4c5a-b21d-03d04861d91f · inbound

EfficientGFormer: Multimodal Brain Tumor Segmentation via Pruned Graph-Augmented Transformer cites this paper.

EfficientGFormer: Multimodal Brain Tumor Segmentation via Pruned Graph-Augmented Transformer Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T05:39:22.733935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78ffe92d-2833-42e6-b973-bacb9e52f0f6 · inbound

Why Relational Graphs Will Save the Next Generation of Vision Foundation Models? cites this paper.

Why Relational Graphs Will Save the Next Generation of Vision Foundation Models? Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T16:31:57.897705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4daebf27-7815-4d2b-b583-7eadb40c3367 · inbound

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer cites this paper.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T14:12:18.173364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ed02acc-5ec6-4477-bf77-2f672869aea7 · inbound

A Space-Time Transformer for Precipitation Nowcasting cites this paper.

A Space-Time Transformer for Precipitation Nowcasting Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T22:19:23.979118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95930b7e-b076-455a-8310-cdcd6490ac7a · inbound

Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI cites this paper.

Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T00:20:55.217799Z

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 b90d1073-4717-4d3b-b31e-84266ae587ba · inbound

AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer cites this paper.

AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:06:08.126622Z

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 cc286412-6f5e-4475-8133-a4d1e90c3fcd · inbound

Align then Refine: Text-Guided 3D Prostate Lesion Segmentation cites this paper.

Align then Refine: Text-Guided 3D Prostate Lesion Segmentation Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:05:23.502049Z

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 3f689548-d9e8-4642-8d8d-d67e10dcdd44 · inbound

MedFlowSeg: Flow Matching for Medical Image Segmentation with Frequency-Aware Attention cites this paper.

MedFlowSeg: Flow Matching for Medical Image Segmentation with Frequency-Aware Attention Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:46:15.965846Z

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 3b1b67ef-c916-4a41-96e6-b398e4f1e4a7 · inbound

SGP-SAM: Self-Gated Prompting for Transferring 3D Segment Anything Models to Lesion Segmentation cites this paper.

SGP-SAM: Self-Gated Prompting for Transferring 3D Segment Anything Models to Lesion Segmentation Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:51:46.295163Z

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 e5d0bb69-9669-4aa2-bd0a-0c1371529a87 · inbound

Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging cites this paper.

Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 103

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T05:29:47.257004Z

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 8bc86f06-5cd1-469a-9cbc-43c723a100b7 · inbound

A Multimodal 3D Foundation Model for Light Sheet Fluorescence Microscopy Enables Few-Shot Segmentation, Classification, and Deblurring cites this paper.

A Multimodal 3D Foundation Model for Light Sheet Fluorescence Microscopy Enables Few-Shot Segmentation, Classification, and Deblurring Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T23:04:01.079381Z

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 64990f6a-0df5-45ca-a473-342d2afd194a · inbound

Efficient Transformer-Based Localized Patch Sampling for Choroid Plexus Segmentation in Multiple Sclerosis cites this paper.

Efficient Transformer-Based Localized Patch Sampling for Choroid Plexus Segmentation in Multiple Sclerosis Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:26:26.472532Z

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 4307c43f-dfc9-493d-8f6a-a422a6abe521 · inbound

MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models cites this paper.

MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:46:56.588632Z

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 7abc9be7-94b6-42df-bc46-701deec4a034 · inbound

HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT cites this paper.

HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T03:19:30.115019Z

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 3da16022-b8cb-4d0d-83ed-255aed1ff833 · inbound

MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation cites this paper.

MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-04T14:39:57.800068Z

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 39eda259-1445-4174-84a1-5148994b27b8 · inbound

Anatomy-Guided Residual Motion Diffusion for Controllable 4D Cardiac MRI Synthesis cites this paper.

Anatomy-Guided Residual Motion Diffusion for Controllable 4D Cardiac MRI Synthesis Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-26T05:18:59.689362Z

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 620919ba-c8a7-44b2-a8db-d8a82c720da7 · inbound

AuricularWorld: Hierarchical Action-Guided World Modeling for Fine-Grained Auricular Structure Segmentation from CT Scans cites this paper.

AuricularWorld: Hierarchical Action-Guided World Modeling for Fine-Grained Auricular Structure Segmentation from CT Scans Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 52

Resolution: unresolved; no resolver link, observed 2026-07-31T05:50:17.928183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.