Structure-Preserving Medical Image Generation from a Latent Graph Representation | Paper Citation Record

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This paper cites An overview of deep learning in medical imaging,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation An overview of deep learning in medical imaging,

Reference 1

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This paper cites A review of deep learning-based multiple- lesion recognition from medical images: Classification, detection and segmentation,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation A review of deep learning-based multiple- lesion recognition from medical images: Classification, detection and segmentation,

Reference 2

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This paper cites MedMNIST v2 – A large-scale lightweight benchmark for 2D and 3D biomedical image classification,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation MedMNIST v2 – A large-scale lightweight benchmark for 2D and 3D biomedical image classification,

Reference 3

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This paper cites Medical image segmentation using deep learning: A survey,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Medical image segmentation using deep learning: A survey,

Reference 4

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This paper cites Semi-supervised medical image segmentation via cross teaching between CNN and transformer,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Semi-supervised medical image segmentation via cross teaching between CNN and transformer,

Reference 5

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This paper cites Deep learning models in medical image analysis,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Deep learning models in medical image analysis,

Reference 6

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This paper cites Literature review: Efficient deep neural networks tech- niques for medical image analysis,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Literature review: Efficient deep neural networks tech- niques for medical image analysis,

Reference 7

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This paper cites A survey on image data augmen- tation for deep learning,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation A survey on image data augmen- tation for deep learning,

Reference 8

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Observation 4285dfa4-45f9-45a7-8a01-250cc6bc2a4b · outbound

This paper cites Medical image data augmentation: Techniques, compar- isons and interpretations,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Medical image data augmentation: Techniques, compar- isons and interpretations,

Reference 9

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Observation c5ba5f6e-9dbc-475e-bde9-e81c88d61f4a · outbound

This paper cites The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning.

Structure-Preserving Medical Image Generation from a Latent Graph Representation The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning

Reference 10

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This paper cites Data augmentation for medical imaging: A systematic literature review,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Data augmentation for medical imaging: A systematic literature review,

Reference 11

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Observation d3e994db-434a-43c1-adb1-d5b1af34c775 · outbound

This paper cites Deep learning ap- proaches for data augmentation in medical imaging: A review,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Deep learning ap- proaches for data augmentation in medical imaging: A review,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:10.683744Z

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Observation c0a7c85b-4979-4308-b3b0-3b7c0e763d51 · outbound

This paper cites Autoencoders and variational autoencoders in medical image analysis,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Autoencoders and variational autoencoders in medical image analysis,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:10.505106Z

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Observation f7d1bc5d-93b6-4471-9dab-4ae00e0f64b4 · outbound

This paper cites Medical image generation using generative adversarial networks: A review,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Medical image generation using generative adversarial networks: A review,

Reference 14

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Observation 17e04e9d-b89a-4cdc-bb5b-e9a5ab2e7369 · outbound

This paper cites Diffusion Models for Medical Image Analysis: A Comprehensive Survey.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Diffusion Models for Medical Image Analysis: A Comprehensive Survey

Reference 15

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This paper cites A review of medical image data augmentation techniques for deep learning applications,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation A review of medical image data augmentation techniques for deep learning applications,

Reference 16

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Observation 5d0b0a4f-077a-4f83-a879-e24a148ea3b2 · outbound

This paper cites Adaptive augmenta- tion of medical data using independently conditional variational auto- encoders,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Adaptive augmenta- tion of medical data using independently conditional variational auto- encoders,

Reference 17

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This paper cites FMRI data augmentation via synthesis,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation FMRI data augmentation via synthesis,

Reference 18

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Observation e19743c2-4080-447f-9f89-2e54b3c036e9 · outbound

This paper cites Data augmentation in high dimensional low sample size setting using a geometry-based variational autoencoder,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Data augmentation in high dimensional low sample size setting using a geometry-based variational autoencoder,

Reference 19

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Observation ed409328-d826-45f4-8997-3951e0d7cb9c · outbound

This paper cites Brain lesion synthesis via progressive adversarial variational auto-encoder,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Brain lesion synthesis via progressive adversarial variational auto-encoder,

Reference 20

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Observation d884472d-ba3a-41df-a8ee-3e33a8db2f38 · outbound

This paper cites Seeing what a gan cannot generate,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Seeing what a gan cannot generate,

Reference 21

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This paper cites Veegan: Reducing mode collapse in gans using implicit variational learning,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Veegan: Reducing mode collapse in gans using implicit variational learning,

Reference 22

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Observation efe3a9d5-37e7-42d0-94c3-1ea63925efc2 · outbound

This paper cites Towards foundation models learned from anatomy in medical imaging via self- supervision,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Towards foundation models learned from anatomy in medical imaging via self- supervision,

Reference 23

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This paper cites Nscgcn: A novel deep gcn model to diagnosis covid-19,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Nscgcn: A novel deep gcn model to diagnosis covid-19,

Reference 24

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This paper cites Cerebrovascular segmentation model based on spatial attention-guided 3D inception U-Net with multi- directional MIPs,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Cerebrovascular segmentation model based on spatial attention-guided 3D inception U-Net with multi- directional MIPs,

Reference 25

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This paper cites VCNet: Hybrid deep learning model for detection and classification of lung carcinoma using chest radiographs,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation VCNet: Hybrid deep learning model for detection and classification of lung carcinoma using chest radiographs,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:08.683344Z

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This paper cites Multiclass convolution neural network for classification of COVID-19 CT images,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Multiclass convolution neural network for classification of COVID-19 CT images,

Reference 27

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This paper cites Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images,

Reference 28

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This paper cites IIMFCBM: Intelligent integrated model for feature extraction and classification of brain tumors using MRI clinical imaging data in IoT-healthcare,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation IIMFCBM: Intelligent integrated model for feature extraction and classification of brain tumors using MRI clinical imaging data in IoT-healthcare,

Reference 29

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This paper cites Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets,

Reference 30

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This paper cites BI-RADS-based classification of mammographic soft tissue opacities using a deep convolutional neural network,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation BI-RADS-based classification of mammographic soft tissue opacities using a deep convolutional neural network,

Reference 31

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Observation f7a64c01-6673-4b6d-a40a-2476f627563f · outbound

This paper cites CNN based fundus images classi- fication for glaucoma identification,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation CNN based fundus images classi- fication for glaucoma identification,

Reference 32

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This paper cites Generative adversarial networks in medical image augmentation: A review,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Generative adversarial networks in medical image augmentation: A review,

Reference 33

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This paper cites Synthesizing anonymized and labeled TOF- MRA patches for brain vessel segmentation using generative adversarial networks,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Synthesizing anonymized and labeled TOF- MRA patches for brain vessel segmentation using generative adversarial networks,

Reference 34

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Observation fd813b89-ebc0-4849-8c3a-218f4f35f5ca · outbound

This paper cites MM-GAN: 3D MRI data augmentation for medical image segmentation via generative adversarial networks,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation MM-GAN: 3D MRI data augmentation for medical image segmentation via generative adversarial networks,

Reference 35

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This paper cites A framework for in-vivo human brain tumor detection using image augmentation and hybrid features,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation A framework for in-vivo human brain tumor detection using image augmentation and hybrid features,

Reference 36

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Observation da8d7ec1-034f-4753-9a53-ad56abf0d82c · outbound

This paper cites Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks,

Reference 37

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This paper cites Breast cancer detection using GAN for limited labeled dataset,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Breast cancer detection using GAN for limited labeled dataset,

Reference 38

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This paper cites Leveraging regular fundus images for training UWF fundus diagnosis models via adversarial learning and pseudo-labeling,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Leveraging regular fundus images for training UWF fundus diagnosis models via adversarial learning and pseudo-labeling,

Reference 39

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Observation 99dca77f-e47f-47c5-99f7-6b9fe571d9bb · outbound

This paper cites Synthetic CT image generation of shape-controlled lung 12 cancer using semi-conditional InfoGAN and its applicability for type classification,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Synthetic CT image generation of shape-controlled lung 12 cancer using semi-conditional InfoGAN and its applicability for type classification,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:05.703853Z

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Observation 9418d16c-75cd-44fd-aeeb-e4976c94de7b · outbound

This paper cites Mass image synthesis in mammogram with contextual information based on GANs,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Mass image synthesis in mammogram with contextual information based on GANs,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:05.420064Z

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This paper cites Data augmentation of thyroid ultrasound images using generative adversarial network,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Data augmentation of thyroid ultrasound images using generative adversarial network,

Reference 42

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Observation b8eac6f7-a0dd-414a-8cb2-4d76263125e8 · outbound

This paper cites Brain tumor classi- fication using a combination of variational autoencoders and generative adversarial networks,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Brain tumor classi- fication using a combination of variational autoencoders and generative adversarial networks,

Reference 43

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Observation df2bfa98-41c1-48f0-82c1-f119cabcacb9 · outbound

This paper cites Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling,

Reference 44

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Observation d53e849d-8bb2-4419-8cec-91ec3ff859e3 · outbound

This paper cites Spot the fake lungs: Generating synthetic medical images using neural diffusion models,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Spot the fake lungs: Generating synthetic medical images using neural diffusion models,

Reference 45

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Observation 0ecd8450-37b5-4b09-b484-d4672f932e4b · outbound

This paper cites Brain imaging generation with latent diffusion models,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Brain imaging generation with latent diffusion models,

Reference 46

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 301346d1-4ec0-49fd-862b-a8f7da8eaa1b · outbound

This paper cites Can segmentation models be trained with fully synthetically generated data?.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Can segmentation models be trained with fully synthetically generated data?

Reference 47

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Observation bccddf36-b082-49b6-9d90-e6bd1ae18e4d · outbound

This paper cites Brain tumor segmentation using synthetic mr images-a comparison of gans and diffusion models,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Brain tumor segmentation using synthetic mr images-a comparison of gans and diffusion models,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:03.850104Z

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Observation 12a3324c-8140-4dc9-b90d-fcd5cac8100a · outbound

This paper cites Novel multi-site graph con- volutional network with supervision mechanism for covid-19 diagnosis from x-ray radiographs,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Novel multi-site graph con- volutional network with supervision mechanism for covid-19 diagnosis from x-ray radiographs,

Reference 49

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Source-reported events for the cited work

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Observation b70db26d-deb6-4339-b3b9-fad28d21bc2e · outbound

This paper cites Frechet inception distance (FID) for evaluating GANs,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Frechet inception distance (FID) for evaluating GANs,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:03.571995Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 71b76815-404a-4829-8cc6-d66127a07f2b · outbound

This paper cites Deep ViT Features as Dense Visual Descriptors.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Deep ViT Features as Dense Visual Descriptors

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T17:47:01.461255Z

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Observation a297c10e-53c3-49ce-a628-1b5bb80ec417 · outbound

This paper cites Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:03.431176Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6171d765-b62f-4a0d-836f-6b0551826eb8 · outbound

This paper cites Conditional image synthesis with auxiliary classifier gans,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Conditional image synthesis with auxiliary classifier gans,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:03.298274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 79080f71-bedb-4220-b52b-dc7e4d58ad49 · outbound

This paper cites Segan: Adversarial network with multi-scale l 1 loss for medical image segmentation,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Segan: Adversarial network with multi-scale l 1 loss for medical image segmentation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:03.148551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5c9c5848-8609-4fc9-8d37-293954631945 · outbound

This paper cites Identifying medical diagnoses and treatable diseases by image-based deep learning,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Identifying medical diagnoses and treatable diseases by image-based deep learning,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:02.964806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 14421661-d68b-4028-bfd1-fb0501059fb6 · outbound

This paper cites Shiraishi, S.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Shiraishi, S

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:02.809870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 83506a1a-d626-432a-840f-2db2e300f853 · outbound

This paper cites Inductive representation learning on large graphs,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Inductive representation learning on large graphs,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T17:47:01.865062Z

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Observation 7dc18cfa-9ab3-4359-904e-e705da5baad9 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Structure-Preserving Medical Image Generation from a Latent Graph Representation How Powerful are Graph Neural Networks?

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T17:47:01.919951Z

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Observation 5671e8c9-3fae-48c2-ae20-da739d8800a9 · outbound

This paper cites Self-attention graph pooling,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Self-attention graph pooling,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:02.647940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c3605898-8079-4a21-9b27-e0dad41da0af · outbound

This paper cites Towards graph pooling by edge contraction,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Towards graph pooling by edge contraction,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:02.514448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 70d3684e-9bd6-4e42-9905-8ed64adc58ce · outbound

This paper cites Graph attention networks,.

Structure-Preserving Medical Image Generation from a Latent Graph Representation Graph attention networks,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:47:02.362938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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