CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography | Paper Citation Record

Observation dfa9728b-23fe-4c0f-bc07-6d583b35e104 · outbound

This paper cites The application of deep learning for the segmentation and classifi- cation of coronary arteries,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography The application of deep learning for the segmentation and classifi- cation of coronary arteries,

Reference 1

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Observation e8cf62b3-5026-482c-8f43-d1f34d552f82 · outbound

This paper cites The SYNTAX score: An angiographic tool grading the complexity of coronary artery disease,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography The SYNTAX score: An angiographic tool grading the complexity of coronary artery disease,

Reference 2

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Observation aaae9957-33fa-4b8b-8bc4-9c79a533fac4 · outbound

This paper cites Progressive perception learning for main coronary segmentation in X-Ray angiography,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Progressive perception learning for main coronary segmentation in X-Ray angiography,

Reference 3

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Observation dd8a0aa7-a013-4b6f-a338-2af321338e84 · outbound

This paper cites Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography,

Reference 4

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Observation ac25a277-21de-4c2a-81ac-41985d0e21b0 · outbound

This paper cites T-Net: Nested encoder–decoder architecture for the main vessel segmen- tation in coronary angiography,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography T-Net: Nested encoder–decoder architecture for the main vessel segmen- tation in coronary angiography,

Reference 5

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Observation 7a157c65-f0a3-4ad9-88d9-32cc41626287 · outbound

This paper cites Main coronary vessel segmentation using deep learning in smart medical,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Main coronary vessel segmentation using deep learning in smart medical,

Reference 6

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Observation c912adc8-63ab-4754-a6be-e7981c83b95c · outbound

This paper cites Deep learning model for coronary artery segmentation and quan- titative stenosis detection in angiographic images,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Deep learning model for coronary artery segmentation and quan- titative stenosis detection in angiographic images,

Reference 7

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Observation 56b52721-47c0-455d-9388-bf53224f6138 · outbound

This paper cites Coronary angiography im- age segmentation based on PSPNet,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Coronary angiography im- age segmentation based on PSPNet,

Reference 8

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Observation 359c0e31-aed6-486a-b680-72bb3c3c2f62 · outbound

This paper cites G2ViT: Graph neu- ral network-guided vision transformer en- hanced network for retinal vessel and coro- nary angiograph segmentation,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography G2ViT: Graph neu- ral network-guided vision transformer en- hanced network for retinal vessel and coro- nary angiograph segmentation,

Reference 9

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Observation 2e416cdd-fbba-4524-a66f-25679d65ae29 · outbound

This paper cites Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic im- ages,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic im- ages,

Reference 10

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Observation 358cc6c0-8817-46d0-991c-932f06a1e4dd · outbound

This paper cites HR-UMamba++: A high- resolution multi-directional mamba frame- work for coronary artery segmentation in X-Ray coronary angiography,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography HR-UMamba++: A high- resolution multi-directional mamba frame- work for coronary artery segmentation in X-Ray coronary angiography,

Reference 11

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Observation 3dfe0903-748a-4f80-b370-7cde0705d19f · outbound

This paper cites Dataset for automatic region-based coronary artery disease diag- nostics using X-Ray angiography images,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Dataset for automatic region-based coronary artery disease diag- nostics using X-Ray angiography images,

Reference 12

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Observation 94bf9c6a-344c-470e-8e8d-0d9b91e0dde8 · outbound

This paper cites Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning

Reference 13

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Observation df342d50-c545-40ee-8808-bc15fd131936 · outbound

This paper cites MPSeg : Multi-Phase strategy for coronary artery Segmentation.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography MPSeg : Multi-Phase strategy for coronary artery Segmentation

Reference 14

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Observation 912001d5-eefc-4a91-a273-9ca0e3270671 · outbound

This paper cites YOLO-Angio: An Algorithm for Coronary Anatomy Segmentation.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography YOLO-Angio: An Algorithm for Coronary Anatomy Segmentation

Reference 15

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Observation 7aae30c9-2eeb-440f-827f-d1100002e64b · outbound

This paper cites Accurate segmentation and la- beling of coronary artery segments in X- Ray angiography with an improved UNet- based cGAN architecture,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Accurate segmentation and la- beling of coronary artery segments in X- Ray angiography with an improved UNet- based cGAN architecture,

Reference 16

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Observation fda0d9cd-f12f-41fe-bf99-71d1e31eb762 · outbound

This paper cites UENet: A novel generative adversarial network for angiography image segmentation,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography UENet: A novel generative adversarial network for angiography image segmentation,

Reference 17

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Observation 4a3c9cb1-cefb-4d3c-ac9e-b17af971fcb4 · outbound

This paper cites Training and validation of a deep learning architecture for the auto- matic analysis of coronary angiography,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Training and validation of a deep learning architecture for the auto- matic analysis of coronary angiography,

Reference 18

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Observation d661aa61-0f46-4254-8682-0a5b9bd4cd72 · outbound

This paper cites Coronary artery vascular segmentation on limited data via pseudo-precise la- bel,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Coronary artery vascular segmentation on limited data via pseudo-precise la- bel,

Reference 19

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Observation 1996e721-9c9d-4aad-a8d0-aba99579aa59 · outbound

This paper cites AGMN: Association graph-based graph matching network for 15 coronary artery semantic labeling on inva- sive coronary angiograms,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography AGMN: Association graph-based graph matching network for 15 coronary artery semantic labeling on inva- sive coronary angiograms,

Reference 20

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Observation 8562f493-0375-4b54-907a-b47a49f22e4d · outbound

This paper cites EAGMN: Coronary artery se- mantic labeling using edge attention graph matching network,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography EAGMN: Coronary artery se- mantic labeling using edge attention graph matching network,

Reference 21

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Observation e082e4fb-6f9c-4cf0-9643-1d718b734fd7 · outbound

This paper cites Multi-graphgraphmatchingfor coronary artery semantic labeling in inva- sive coronary angiograms,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Multi-graphgraphmatchingfor coronary artery semantic labeling in inva- sive coronary angiograms,

Reference 22

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Observation 897cd54a-9da7-4727-9112-d0834ee5a1a6 · outbound

This paper cites HAGMN-UQ: Hyper associa- tion graph matching network with uncer- tainty quantification for coronary artery semantic labeling,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography HAGMN-UQ: Hyper associa- tion graph matching network with uncer- tainty quantification for coronary artery semantic labeling,

Reference 23

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Observation 8bf8191f-98d6-4184-b2ab-45d6c91ad4af · outbound

This paper cites Diffusion-based user-guided data augmentation for coronary stenosis detection,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Diffusion-based user-guided data augmentation for coronary stenosis detection,

Reference 24

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Observation 40b4ad07-b137-49c3-869a-3fd94986c74f · outbound

This paper cites Self-supervised ves- sel segmentation via adversarial learn- ing,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Self-supervised ves- sel segmentation via adversarial learn- ing,

Reference 25

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Observation 1fd12ac1-7f9c-47ce-abf6-ba0c7ee5c998 · outbound

This paper cites Auto- matic segmentation of coronary arteries in X-Ray angiograms using multiscale anal- ysis and artificial neural networks,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Auto- matic segmentation of coronary arteries in X-Ray angiograms using multiscale anal- ysis and artificial neural networks,

Reference 26

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Observation 015277f3-1cb3-41d4-bf54-d661cc7dacc3 · outbound

This paper cites CoronaryDominance: Angiogram dataset for coronary domi- nance classification,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography CoronaryDominance: Angiogram dataset for coronary domi- nance classification,

Reference 27

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Observation d03dc945-029e-4d4b-ac29-ad89c82175e2 · outbound

This paper cites CardioSyntax: End-to-end SYNTAX score prediction - dataset, benchmark and method,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography CardioSyntax: End-to-end SYNTAX score prediction - dataset, benchmark and method,

Reference 28

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Observation 5d5f56ff-7726-4f62-ae31-9cdf34e423b7 · outbound

This paper cites Digital imag- ing and communications in medicine (DICOM),.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Digital imag- ing and communications in medicine (DICOM),

Reference 29

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Observation a461d1fd-d024-43f0-aa9c-f1b13ce138c1 · outbound

This paper cites 2026 heart disease and stroke statistics: A re- port of US and global data from the American Heart Association,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography 2026 heart disease and stroke statistics: A re- port of US and global data from the American Heart Association,

Reference 30

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Observation 5c837e55-0ce7-4379-94c8-8f5f34fb6227 · outbound

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

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography U-Net: Convolutional networks for biomedical image segmentation,

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This paper cites Attention U-Net: Learn- ing where to look for the pancreas,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Attention U-Net: Learn- ing where to look for the pancreas,

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This paper cites 3D MRI brain tu- mor segmentation using autoencoder regularization,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography 3D MRI brain tu- mor segmentation using autoencoder regularization,

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Observation 285fdd51-3f5b-44f7-ad12-63351d6e4d2a · outbound

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

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography nnU- Net: A self-configuring method for deep learning-based biomedical image segmen- tation,

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This paper cites Pyramid scene parsing network,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Pyramid scene parsing network,

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This paper cites Fea- ture pyramid networks for object de- tection,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Fea- ture pyramid networks for object de- tection,

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This paper cites Unified perceptual parsing for scene understanding,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Unified perceptual parsing for scene understanding,

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This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Rethinking Atrous Convolution for Semantic Image Segmentation

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Observation 5f9a0790-1689-4818-946a-c1d944fe07e0 · outbound

This paper cites Encoder- decoder with atrous separable convolution forsemanticimagesegmentation,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Encoder- decoder with atrous separable convolution forsemanticimagesegmentation,

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This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

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This paper cites SegFormer: Simple and efficient design for semantic segmentation with transformers,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography SegFormer: Simple and efficient design for semantic segmentation with transformers,

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This paper cites SwinUNETR-V2: Stronger swin transformers with stagewise convolutions for 3D medical image seg- mentation,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography SwinUNETR-V2: Stronger swin transformers with stagewise convolutions for 3D medical image seg- mentation,

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This paper cites ConvNeXt V2: Co- designing and scaling convnets with masked autoencoders,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography ConvNeXt V2: Co- designing and scaling convnets with masked autoencoders,

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This paper cites SegNeXt: Rethinking convolutional attention design for semantic segmentation,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography SegNeXt: Rethinking convolutional attention design for semantic segmentation,

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This paper cites MedNeXt: Transformer- driven scaling of convnets for medical im- age segmentation,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography MedNeXt: Transformer- driven scaling of convnets for medical im- age segmentation,

Reference 45

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This paper cites Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs

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Observation 8c76ee45-b622-49ec-9f0b-36c45e8d5c30 · outbound

This paper cites Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation

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This paper cites SegMamba: Long-range sequen- tial modeling mamba for 3D medical im- age segmentation,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography SegMamba: Long-range sequen- tial modeling mamba for 3D medical im- age segmentation,

Reference 48

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This paper cites TorchXRayVision: A library of chest X-Ray datasets and mod- els,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography TorchXRayVision: A library of chest X-Ray datasets and mod- els,

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This paper cites RadImageNet: An open radiologic deep learning research dataset 17 for effective transfer learning,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography RadImageNet: An open radiologic deep learning research dataset 17 for effective transfer learning,

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This paper cites Averaging weights leads to wider optima and better generaliza- tion,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Averaging weights leads to wider optima and better generaliza- tion,

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This paper cites Metrics for evaluating 3D medical image segmenta- tion: Analysis, selection, and tool,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Metrics for evaluating 3D medical image segmenta- tion: Analysis, selection, and tool,

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This paper cites Comparing images using the Hausdorff distance,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Comparing images using the Hausdorff distance,

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This paper cites clDice - a novel topology- preserving loss function for tubular struc- ture segmentation,.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography clDice - a novel topology- preserving loss function for tubular struc- ture segmentation,

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This paper cites Lumen diameter of nor- mal human coronary arteries. influence of age, sex, anatomic variation, and left ven- tricular hypertrophy or dilation.

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography Lumen diameter of nor- mal human coronary arteries. influence of age, sex, anatomic variation, and left ven- tricular hypertrophy or dilation

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