Paper Citation Record · LEDGER

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models

As of 21 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2505.07364.

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pith.paper-citation-record.v1
2505.07364 v1

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Outbound references

Observation 07d1f8fb-5904-4b5c-86cb-0aae57b5ec2b · outbound

This paper cites Regularized siamese neural network for unsupervised outlier detection on brain multiparametricmagneticresonanceimaging:applicationtoepilepsy lesion screening.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Regularized siamese neural network for unsupervised outlier detection on brain multiparametricmagneticresonanceimaging:applicationtoepilepsy lesion screening

Reference 1

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This paper cites Unsupervised medical image translation using cycle-medgan,in:201927thEuropeanSignalProcessingConference (EUSIPCO), IEEE.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Unsupervised medical image translation using cycle-medgan,in:201927thEuropeanSignalProcessingConference (EUSIPCO), IEEE

Reference 2

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This paper cites Autoencoders for unsupervised anomaly segmentation in brain MR images:Acomparativestudy.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Autoencoders for unsupervised anomaly segmentation in brain MR images:Acomparativestudy

Reference 3

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This paper cites Journal of Medical Imaging 8.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Journal of Medical Imaging 8

Reference 4

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This paper cites Resvit:Residualvisiontrans- formers for multimodal medical image synthesis.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Resvit:Residualvisiontrans- formers for multimodal medical image synthesis

Reference 5

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This paper cites Deep learning based synthesis of mri, ct and pet: Review and analysis.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Deep learning based synthesis of mri, ct and pet: Review and analysis

Reference 6

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This paper cites Deep-learning predicted pet can be subtracted from the true clinical fluorodeoxyglucose pet co-registered to mri to identify the epileptogenic focus in focal epilepsy.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Deep-learning predicted pet can be subtracted from the true clinical fluorodeoxyglucose pet co-registered to mri to identify the epileptogenic focus in focal epilepsy

Reference 7

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This paper cites Distance-based detection of out-of-distribution silent failures for covid-19 lung lesion segmentation.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Distance-based detection of out-of-distribution silent failures for covid-19 lung lesion segmentation

Reference 8

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This paper cites Generative adversarial nets, in: Advances in neural information processing systems, pp.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Generative adversarial nets, in: Advances in neural information processing systems, pp

Reference 9

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Observation 34d3c2d4-c6e5-4ed1-bb29-9ed3eb5203bb · outbound

This paper cites Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe

Reference 10

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This paper cites Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations

Reference 11

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This paper cites One model to synthesize them all: Multi-contrast multi- scale transformer for missing data imputation.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models One model to synthesize them all: Multi-contrast multi- scale transformer for missing data imputation

Reference 12

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This paper cites Least squares generative adversarial networks, in: Proceedings of the IEEE conference on computer vision (ICCV), pp.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Least squares generative adversarial networks, in: Proceedings of the IEEE conference on computer vision (ICCV), pp

Reference 13

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This paper cites On the pitfalls of using the residual as anomaly score, in: Medical Imaging with Deep Learning (MIDL), 2022 International Conference on.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models On the pitfalls of using the residual as anomaly score, in: Medical Imaging with Deep Learning (MIDL), 2022 International Conference on

Reference 14

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This paper cites Cermep- idb-mrxfdg: a database of 37 normal adult human brain [18f]fdg pet, t1 and flair mri, and ct images available for research.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Cermep- idb-mrxfdg: a database of 37 normal adult human brain [18f]fdg pet, t1 and flair mri, and ct images available for research

Reference 15

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GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Unresolved cited work

Reference 16

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GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Unresolved cited work

Reference 17

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This paper cites Spatially- constrained fisher representation for brain disease identification with incompletemulti-modalneuroimages.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Spatially- constrained fisher representation for brain disease identification with incompletemulti-modalneuroimages

Reference 18

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This paper cites Disease-image-specific learningfordiagnosis-orientedneuroimagesynthesiswithincomplete multi-modalitydata.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Disease-image-specific learningfordiagnosis-orientedneuroimagesynthesiswithincomplete multi-modalitydata

Reference 19

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This paper cites Unsupervisedbrainimaging3danomalyde- tectionandsegmentationwithtransformers.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Unsupervisedbrainimaging3danomalyde- tectionandsegmentationwithtransformers

Reference 20

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GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Unresolved cited work

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GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Unresolved cited work

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This paper cites Neural computation 13, 1443–1471.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Neural computation 13, 1443–1471

Reference 23

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GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Unresolved cited work

Reference 24

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This paper cites MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease

Reference 25

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This paper cites Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Reference 26

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This paper cites Image qualityassessment:fromerrorvisibilitytostructuralsimilarity.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Image qualityassessment:fromerrorvisibilitytostructuralsimilarity

Reference 27

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This paper cites Predictingpet-deriveddemyelinationfrommul- timodal mri using sketcher-refiner adversarial training for multiple sclerosis.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Predictingpet-deriveddemyelinationfrommul- timodal mri using sketcher-refiner adversarial training for multiple sclerosis

Reference 28

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This paper cites Pro- posal for a new classification of outcome with respect to epileptic seizures following epilepsy surgery.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Pro- posal for a new classification of outcome with respect to epileptic seizures following epilepsy surgery

Reference 29

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This paper cites Gener- ative adversarial networks for noise reduction in low-dose ct.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Gener- ative adversarial networks for noise reduction in low-dose ct

Reference 30

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This paper cites Generative adversarial networks: A primer for radiologists.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Generative adversarial networks: A primer for radiologists

Reference 31

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This paper cites Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model

Reference 32

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GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Unresolved cited work

Reference 33

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This paper cites Bpgan: Brain pet synthesis from mri using generative adversarial network for multi-modal alzheimer’s disease diagnosis.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Bpgan: Brain pet synthesis from mri using generative adversarial network for multi-modal alzheimer’s disease diagnosis

Reference 34

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This paper cites The unreasonableeffectivenessofdeepfeaturesasaperceptualmetric,in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models The unreasonableeffectivenessofdeepfeaturesasaperceptualmetric,in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

Reference 35

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This paper cites Unpaired image- to-image translation using cycle-consistent adversarial networks, in: Computer Vision (ICCV), 2017 IEEE International Conference on.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Unpaired image- to-image translation using cycle-consistent adversarial networks, in: Computer Vision (ICCV), 2017 IEEE International Conference on

Reference 36

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GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Unresolved cited work

Reference 37

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This paper cites Global Image-Based Unsupervised Anomaly Detec- tion in MR Brain Scans of Early Parkinsonian Patients, in: Machine Learning in Clinical Neuroimaging, Cham.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Global Image-Based Unsupervised Anomaly Detec- tion in MR Brain Scans of Early Parkinsonian Patients, in: Machine Learning in Clinical Neuroimaging, Cham

Reference 2021

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