Paper Citation Record · LEDGER

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models

As of 13 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2506.21245.

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

pith.paper-citation-record.v1
2506.21245 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:33:54.354420Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

25 of 25 outbound references displayed

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verified fuzzy5
unresolved7
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fd613903-4eb4-4a32-ba52-0710931d7886 · outbound

This paper cites Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.589430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b9022ec3-ef3e-4f02-81bd-f953f34943cd · outbound

This paper cites BrainGAN: Brain MRI Image Generation and Classification Framework Using GAN Architectures and CNN Models.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models BrainGAN: Brain MRI Image Generation and Classification Framework Using GAN Architectures and CNN Models

Reference 2

Resolution: verified exact; doi, observed 2026-08-06T22:33:55.172820Z

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Observation 23bb11c5-e303-45b1-b7e5-49be3b62f8b0 · outbound

This paper cites The basics of brain development.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models The basics of brain development

Reference 4

Resolution: verified exact; doi, observed 2026-08-06T22:33:54.993124Z

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Observation 6d3fc5a9-cea6-4cf6-9392-962748bc6129 · outbound

This paper cites Epidemiology of glial and non-glial brain tumours in Europe.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models Epidemiology of glial and non-glial brain tumours in Europe

Reference 5

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Source-reported events for the cited work

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Observation 2a84fc1c-1a9f-4e69-9003-5cf9074be9de · outbound

This paper cites Cancer statistics, 2023.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models Cancer statistics, 2023

Reference 6

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Source-reported events for the cited work

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Observation 5b2e3cbe-896f-4df7-8e37-3519f4a88a1b · outbound

This paper cites State of the art survey on MRI brain tumor segmentation.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models State of the art survey on MRI brain tumor segmentation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.430791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 97ab375d-fdc2-4711-a14d-fe555d86f82f · outbound

This paper cites Tumor-Cut: Segmentation of Brain Tumors on Contrast-Enhanced MR Images for Radiosurgery Applications.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models Tumor-Cut: Segmentation of Brain Tumors on Contrast-Enhanced MR Images for Radiosurgery Applications

Reference 8

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:33:56.569134Z

Source-reported events for the cited work

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Observation 84141f41-67b5-428b-b6b9-82531bb12c3e · outbound

This paper cites Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey

Reference 9

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:33:56.346948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation eedaf147-8384-42f0-bc22-87878e67079f · outbound

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

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models U-net: convolutional networks for biomedical image segmentation

Reference 10

Resolution: malformed identifier; no resolver link, observed 2026-08-06T22:33:51.213182Z

Source-reported events for the cited work

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Observation 70ee2c3a-7401-40d8-aa50-485f9e9a48aa · outbound

This paper cites MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation

Reference 11

Resolution: malformed identifier; no resolver link, observed 2026-08-06T22:33:51.351253Z

Source-reported events for the cited work

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Observation 4721ddfb-429f-45a8-b555-392ecbff5400 · outbound

This paper cites UNet++: A Nested U-Net Architecture for Medical Image Segmentation.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models UNet++: A Nested U-Net Architecture for Medical Image Segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.245626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2df79492-b446-4c84-ba33-781b877781e7 · outbound

This paper cites MM-UNet: A multimodality brain tumor segmentation network in MRI images.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models MM-UNet: A multimodality brain tumor segmentation network in MRI images

Reference 13

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:33:56.059251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 806f2595-2624-4e22-b5ec-22dcaa4d11f1 · outbound

This paper cites Cross-modality deep feature learning for brain tumor segmentation.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models Cross-modality deep feature learning for brain tumor segmentation

Reference 14

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:33:55.808892Z

Source-reported events for the cited work

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Observation 2ce548d1-8def-456d-9e7f-5db938962c33 · outbound

This paper cites How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:53.442122Z

Source-reported events for the cited work

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Observation 1e40ee2d-e24b-4b10-9c8c-1442ba8f4dca · outbound

This paper cites nnU-Net: a self-configuring method for deep learning-based biomedical image segmen- tation.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models nnU-Net: a self-configuring method for deep learning-based biomedical image segmen- tation

Reference 16

Resolution: malformed identifier; no resolver link, observed 2026-08-06T22:33:53.529787Z

Source-reported events for the cited work

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Observation b3652284-9f7e-49e5-9086-c8a9f8bd47c9 · outbound

This paper cites Accessed: 2024-12-05.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models Accessed: 2024-12-05

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.049207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3996b354-3f90-4271-97f0-dd48bd4754e5 · outbound

This paper cites The preprocessed connectomes project repository of manually corrected skull-stripped T1- weighted anatomical MRI data.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models The preprocessed connectomes project repository of manually corrected skull-stripped T1- weighted anatomical MRI data

Reference 18

Resolution: verified exact; doi, observed 2026-08-06T22:33:54.833840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dca1c5d4-edee-4aa3-a0ca-ec0e5a3db6c8 · outbound

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

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Reference 19

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Source-reported events for the cited work

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Observation 0acf60d9-349d-4f10-8061-cb71140cd9ae · outbound

This paper cites Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:53.865431Z

Source-reported events for the cited work

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Observation 944ebf57-c700-4dfb-8a3a-3fa4f7d76bd4 · outbound

This paper cites Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:53.924430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3326ef7e-0aed-42f7-8a95-cd50d49c9774 · outbound

This paper cites 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:54.031056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1edf4994-1de1-46e2-b37b-3bbc8bd111c9 · outbound

This paper cites ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation

Reference 23

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:33:55.538391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7ffe5847-800e-4ea1-9fb7-d8eb31018557 · outbound

This paper cites The Use of Robust Local Hausdorff Distances in Accuracy Assessment for Image Alignment of Brain MRI.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models The Use of Robust Local Hausdorff Distances in Accuracy Assessment for Image Alignment of Brain MRI

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:56.815572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fd62a6e7-10d6-4b7b-ad24-de8c20196986 · outbound

This paper cites A Symmetrical Approach to Brain Tumor Segmen- tation in MRI Using Deep Learning and Threefold Attention Mechanism.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models A Symmetrical Approach to Brain Tumor Segmen- tation in MRI Using Deep Learning and Threefold Attention Mechanism

Reference 25

Resolution: verified exact; doi, observed 2026-08-06T22:33:54.607821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c6e38ef1-e529-4a57-8360-93e1c9f8aab2 · outbound

This paper cites An Ensemble Approach for Brain Tumor Segmentation and Synthesis.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models An Ensemble Approach for Brain Tumor Segmentation and Synthesis

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:54.354420Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.