Paper Citation Record · LEDGER

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention

As of 20 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2506.15562.

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

pith.paper-citation-record.v1
2506.15562 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:55:43.786652Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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 exact4
verified fuzzy18
unresolved11
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6bfe6c09-7aee-4411-aa57-a82b0e97e24a · outbound

This paper cites Global cancer observatory: cancer today.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Global cancer observatory: cancer today

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.678237Z

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Observation d58e0742-eb64-4625-aee2-2819882da2f0 · outbound

This paper cites MR imaging of neoplastic central nervous system lesions: review and recom- mendations for current practice.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention MR imaging of neoplastic central nervous system lesions: review and recom- mendations for current practice

Reference 2

Resolution: verified exact; doi, observed 2026-08-06T23:55:43.883005Z

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Observation b8801de6-d9c8-4557-8d0c-5109cc3bc6d8 · outbound

This paper cites Classification of subtype of acute ischemic stroke: Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Classification of subtype of acute ischemic stroke: Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment

Reference 3

Resolution: verified exact; doi, observed 2026-08-06T23:55:43.865716Z

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Observation 1f461fd5-7ddd-40cf-883f-16964e5ebffb · outbound

This paper cites Simultaneous Truth and Performance Level Estimation (STAPLE): an algorithm for the validation of image segmentation.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Simultaneous Truth and Performance Level Estimation (STAPLE): an algorithm for the validation of image segmentation

Reference 4

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Observation ed15b495-d836-48d5-b2df-337d752ce830 · outbound

This paper cites A survey on deep learning in medical image analysis.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention A survey on deep learning in medical image analysis

Reference 5

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Source-reported events for the cited work

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Observation 03f8c894-be5b-430b-9634-d49975f80d83 · outbound

This paper cites The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.662455Z

Source-reported events for the cited work

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Observation 7175b82b-41d0-4d8b-aaff-19ba3236098c · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention U-net: Convolutional networks for biomedical image segmentation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.643868Z

Source-reported events for the cited work

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Observation a14c89d8-cb5c-49fd-bbf6-bf4c15a93d64 · outbound

This paper cites A Survey of the Self Supervised Learning Mechanisms for Vision Transformers.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention A Survey of the Self Supervised Learning Mechanisms for Vision Transformers

Reference 8

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Source-reported events for the cited work

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Observation afcd0927-6076-4aa1-b263-c260bd91e7ef · outbound

This paper cites A Recent Survey of Vision Transformers for Medical Image Segmentation.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention A Recent Survey of Vision Transformers for Medical Image Segmentation

Reference 9

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Source-reported events for the cited work

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Observation 589b8085-e062-494c-90e4-ad3afec5e4ab · outbound

This paper cites Domain Adaptation for Medical Image Analysis: A Survey.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Domain Adaptation for Medical Image Analysis: A Survey

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T23:55:43.667065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b231d36-da66-48de-aac7-0496f009e579 · outbound

This paper cites Official Journal of the European Union, L 119, 4 May 2016, pp.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Official Journal of the European Union, L 119, 4 May 2016, pp

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.626530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba99937e-8078-4896-a74d-cf39b8fd03ca · outbound

This paper cites MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:55:44.140183Z

Source-reported events for the cited work

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Observation 211baade-e871-4615-9d90-c0d3f7f99e8b · outbound

This paper cites nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomed- ical Image Segmentation.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomed- ical Image Segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.608980Z

Source-reported events for the cited work

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Observation 3f5f62ef-00ef-4986-9a34-3db809eb1b5e · outbound

This paper cites UNETR: Transformers for 3D Medical Image Segmentation.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention UNETR: Transformers for 3D Medical Image Segmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.589623Z

Source-reported events for the cited work

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Observation 1455f798-8781-416e-bb92-20ab2f1d837b · outbound

This paper cites The Liver Tumor Segmentation Benchmark (LiTS).

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention The Liver Tumor Segmentation Benchmark (LiTS)

Reference 15

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Source-reported events for the cited work

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Observation b3e2eed7-a46b-47d0-9217-8ea7a5dcd324 · outbound

This paper cites H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:55:43.698036Z

Source-reported events for the cited work

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Observation 7fe698da-88a6-4ce9-80c2-4543c4b8bb5b · outbound

This paper cites pydicom: An Open Source DICOM Library.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention pydicom: An Open Source DICOM Library

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.572468Z

Source-reported events for the cited work

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Observation 639f11ba-ced3-48bf-8242-6e59a7ec5f31 · outbound

This paper cites A Survey on Image Data Augmentation for Deep Learning.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention A Survey on Image Data Augmentation for Deep Learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.554013Z

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Observation fb2a6360-1bb0-45e4-ae85-faba8fdac770 · outbound

This paper cites Attention is all you need.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Attention is all you need

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.536078Z

Source-reported events for the cited work

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Observation 17cef672-b67b-4a5b-8d77-283ba7238466 · outbound

This paper cites A Stacked Multi-Connection Simple Reducing Net for Brain Tumor Segmen- tation.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention A Stacked Multi-Connection Simple Reducing Net for Brain Tumor Segmen- tation

Reference 20

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T23:55:44.026631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9bcb8eb8-6094-4b8b-a6c2-7b1606e59055 · outbound

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

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T23:55:43.723327Z

Source-reported events for the cited work

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Observation 550f7d84-bb6b-43c0-a45f-c7df351f6c03 · outbound

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

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:55:43.728139Z

Source-reported events for the cited work

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Observation 6ea2ac31-e39b-4fd1-8aab-14f1f61a7c04 · outbound

This paper cites HUT: Hybrid UNet transformer for brain lesion and tumour segmentation.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention HUT: Hybrid UNet transformer for brain lesion and tumour segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.519593Z

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Observation 2b6ab794-8340-4044-8c4d-912933f33169 · outbound

This paper cites HTTU-Net: Hybrid Two Track U-Net for Automatic Brain Tumor Segmentation.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention HTTU-Net: Hybrid Two Track U-Net for Automatic Brain Tumor Segmentation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T23:55:43.737698Z

Source-reported events for the cited work

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Observation 8a80248c-0153-4fc8-bdc2-01eaa32f3137 · outbound

This paper cites Deep residual learning for image recognition.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Deep residual learning for image recognition

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T23:55:43.742592Z

Source-reported events for the cited work

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Observation 9386b3a0-1d42-45d7-bd68-0b8f2583ca61 · outbound

This paper cites Segmentation Models.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Segmentation Models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.490561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8004736e-94a1-4939-aa3f-ba0db63fa4c7 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Imagenet: A large-scale hierarchical image database

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.473376Z

Source-reported events for the cited work

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Observation fbf00f28-c111-4e87-b281-827b7e96e005 · outbound

This paper cites Squeeze-and-excitation networks.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Squeeze-and-excitation networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.455209Z

Source-reported events for the cited work

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Observation af69f30e-a793-4d67-ad76-81d9d790c00f · outbound

This paper cites Cbam: Convolutional block attention module.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Cbam: Convolutional block attention module

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.435701Z

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Observation d587f6c4-bfb0-4f0e-899b-9659675426e2 · outbound

This paper cites Efficient attention: Attention with linear complexities.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Efficient attention: Attention with linear complexities

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.415593Z

Source-reported events for the cited work

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Observation f4fa2002-d26a-4a54-8c49-2443c8d3dd55 · outbound

This paper cites Aggregated residual transformations for deep neural networks.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Aggregated residual transformations for deep neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.398915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4439dcd1-3b22-418a-9e69-cf39efb6ddd9 · outbound

This paper cites V-Net: Fully Convolutional Neu- ral Networks for Volumetric Medical Image Segmentation.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention V-Net: Fully Convolutional Neu- ral Networks for Volumetric Medical Image Segmentation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T23:55:43.777142Z

Source-reported events for the cited work

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Observation d815eb99-9725-42a9-bb75-5151d302098f · outbound

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

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention Unet++: A nested u-net architecture for medical image segmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.381436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1ae142fa-47b5-4711-a756-c0eae0d37d55 · outbound

This paper cites MM-BiFPN: multi-modality fu- sion network with Bi-FPN for MRI brain tumor segmentation.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention MM-BiFPN: multi-modality fu- sion network with Bi-FPN for MRI brain tumor segmentation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:55:44.364049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.