Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:37:30.109235Z
Paper Citation Record · LEDGER
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States
As of 21 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2507.18677.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
42 of 42 outbound references displayed
- verified exact2
- verified fuzzy30
- unresolved10
- parse uncertain0
- malformed identifier0
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation bfc97dbd-ac86-4c71-88f8-65b1b66bedbe · outbound
This paper cites Layer Normalization.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Layer Normalization
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:37:29.695068Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b95098b-e83d-41ca-8adb-ad179b73fc7f · outbound
This paper cites Finite element procedures.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Finite element procedures
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.957574Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 69b16aa3-02fe-4bfb-b050-649d15124d33 · outbound
This paper cites Large strain viscoelastic constitutive models.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Large strain viscoelastic constitutive models
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.922577Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8e765712-7c31-4158-9555-f93bc97aa36c · outbound
This paper cites How Attentive are Graph Attention Networks?.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States How Attentive are Graph Attention Networks?
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:37:29.726441Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1ca579bb-51af-4f2e-996a-abff88f9d6c1 · outbound
This paper cites Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.869644Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation dba78a07-06d8-4c48-bb00-c2649de421dd · outbound
This paper cites Emulation of cardiac mechanics using graph neural networks.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Emulation of cardiac mechanics using graph neural networks
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.821345Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation e146f9bf-c6d1-4247-8e8b-0ddbeb0cf74a · outbound
This paper cites Physics-informed graph neural network emulation of soft-tissue mechanics.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Physics-informed graph neural network emulation of soft-tissue mechanics
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.781616Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 20751b1c-f28b-444f-89e1-dffc52ded535 · outbound
This paper cites Model- based assessment of elastic material parameters in rheumatic heart disease patients and healthy subjects.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Model- based assessment of elastic material parameters in rheumatic heart disease patients and healthy subjects
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.738314Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation e85e8334-38d0-4a7f-996d-167854767c2e · outbound
This paper cites Efficientestimationofpersonalizedbiventricular mechanicalfunctionemployinggradient-basedoptimization.Internationaljournalfornumericalmethodsinbiomedicalengineering34,e2982.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Efficientestimationofpersonalizedbiventricular mechanicalfunctionemployinggradient-basedoptimization.Internationaljournalfornumericalmethodsinbiomedicalengineering34,e2982
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.702694Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 0857f4ca-7803-4858-ab37-a48e36c59b5e · outbound
This paper cites Modeling pathologies of diastolic and systolic heart failure.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Modeling pathologies of diastolic and systolic heart failure
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.659381Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 94becc81-0352-4d7c-b3ef-bdb5321db55f · outbound
This paper cites Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities
Reference 11
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4f1a62e2-bd0d-41e1-96d3-1b90af2c7e6c · outbound
This paper cites an unresolved cited work.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Unresolved cited work
Reference 12
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T14:37:31.607734Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2c78d331-6a8c-42a4-a93e-f55c3fa03937 · outbound
This paper cites Myocardialbiomechanicaleffectsoffetalaorticvalvuloplasty.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Myocardialbiomechanicaleffectsoffetalaorticvalvuloplasty
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.546304Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8ebc5f0e-efbd-4edf-8a85-04714b6f91f3 · outbound
This paper cites Passive material properties of intact ventricular myocardium determined from a cylindrical model.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Passive material properties of intact ventricular myocardium determined from a cylindrical model
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.497888Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation bd237658-7e03-4a85-a65d-bcba6357269c · outbound
This paper cites Inductive representation learning on large graphs.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Inductive representation learning on large graphs
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.466007Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 864eb57c-8f37-4edf-9eee-dd9f0540a2e2 · outbound
This paper cites Constitutive modelling of passive myocardium: a structurally based framework for material characterization.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Constitutive modelling of passive myocardium: a structurally based framework for material characterization
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.437896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 475de5a4-004c-4613-bb74-ae25e5e206da · outbound
This paper cites Semi-Supervised Classification with Graph Convolutional Networks.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Semi-Supervised Classification with Graph Convolutional Networks
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:37:29.853594Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 59085a34-6083-48f7-96ca-c1c00b1b4829 · outbound
This paper cites Patient-specific models of cardiac biomechanics.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Patient-specific models of cardiac biomechanics
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.413777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ddda3a2b-8d6d-47ac-aba8-7757b7761bf7 · outbound
This paper cites Backpropagation applied to handwritten zip code recognition.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Backpropagation applied to handwritten zip code recognition
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.384408Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 135afcf8-df1c-4c2d-b52d-ff767744ae72 · outbound
This paper cites Pytorch-fea: Autograd-enabled finite element analysis methods with applications for biomechanical analysis of human aorta.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Pytorch-fea: Autograd-enabled finite element analysis methods with applications for biomechanical analysis of human aorta
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.358082Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8e6f0258-ef04-4f45-9906-f3c67eda570a · outbound
This paper cites A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero-pressure geometry of human thoracic aorta.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero-pressure geometry of human thoracic aorta
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.312468Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ee499ae6-a067-4df3-b6ed-f8381daefdaa · outbound
This paper cites Journal of computational physics 463, 111266.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Journal of computational physics 463, 111266
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.265179Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b36db499-5c98-4e57-950e-dd6a47778ef7 · outbound
This paper cites Left ventricular shape variation in asymptomatic populations: the multi-ethnic study of atherosclerosis.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Left ventricular shape variation in asymptomatic populations: the multi-ethnic study of atherosclerosis
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.222989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 570c5430-8f57-4448-b8e1-9fbd9bea2b6f · outbound
This paper cites IMC-PINN-FE: A Physics-Informed Neural Network for Patient-Specific Left Ventricular Finite Element Modeling with Image Motion Consistency and Biomechanical Parameter Estimation.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States IMC-PINN-FE: A Physics-Informed Neural Network for Patient-Specific Left Ventricular Finite Element Modeling with Image Motion Consistency and Biomechanical Parameter Estimation
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T14:37:30.471987Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a024b119-bb45-4aa9-828b-69f9e7a41c17 · outbound
This paper cites Effects of using the unloaded configuration in predicting the in vivo diastolic properties of the heart.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Effects of using the unloaded configuration in predicting the in vivo diastolic properties of the heart
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.187126Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1e869351-5bdf-45b5-ac49-009d49b82793 · outbound
This paper cites Learning mesh-based simulation with graph networks, in: International conference on learning representations.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Learning mesh-based simulation with graph networks, in: International conference on learning representations
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.161321Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8ec70d1b-1208-4360-80ef-59931b5a01d3 · outbound
This paper cites PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Reference 27
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d26e12fd-39ff-4b59-b901-12ff808618a2 · outbound
This paper cites An Introduction to Nonlinear Finite Element Analysis: with applications to heat transfer, fluid mechanics, and solid mechanics.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States An Introduction to Nonlinear Finite Element Analysis: with applications to heat transfer, fluid mechanics, and solid mechanics
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.106502Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a41b3223-fd13-49a6-b6b2-bdbf9db31540 · outbound
This paper cites Highspatialresolutionmulti-organfiniteelementmodelingofventricular-arterialcoupling.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Highspatialresolutionmulti-organfiniteelementmodelingofventricular-arterialcoupling
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.068062Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2e4a21e5-1fc5-4f18-901e-78a67cd0bd3d · outbound
This paper cites HeartSimSage: Attention-Enhanced Graph Neural Networks for Accelerating Cardiac Mechanics Modeling.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States HeartSimSage: Attention-Enhanced Graph Neural Networks for Accelerating Cardiac Mechanics Modeling
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:37:29.980080Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dd5630c5-87f6-4837-895f-8130ec2b95bc · outbound
This paper cites Non-invasive in silico determination of ventricular wall pre-straining and characteristic cavity pressures.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Non-invasive in silico determination of ventricular wall pre-straining and characteristic cavity pressures
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T14:37:30.305219Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a33eb13e-327c-4a82-aee3-4eeac50198af · outbound
This paper cites Dropout: a simple way to prevent neural networks from overfitting.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Dropout: a simple way to prevent neural networks from overfitting
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.021887Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2b34eee1-8ec0-4d34-9317-ba7ddcaaee93 · outbound
This paper cites Attention is all you need.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Attention is all you need
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:37:30.005637Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cacd17fd-f736-4790-8e0d-1059c2c167a9 · outbound
This paper cites Graph Attention Networks.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Graph Attention Networks
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:37:30.026330Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dcb46f30-29c5-43cd-9b4c-ea2f3a3bfebe · outbound
This paper cites Image-based predictive modeling of heart mechanics.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Image-based predictive modeling of heart mechanics
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:30.910017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d9cde825-2e03-470e-9988-40d98484eedd · outbound
This paper cites Efficientestimationofload-freeleftventriculargeometryandpassivemyocardialpropertiesusingprincipalcomponentanalysis.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Efficientestimationofload-freeleftventriculargeometryandpassivemyocardialpropertiesusingprincipalcomponentanalysis
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:30.868872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a22b46d7-52eb-4b4b-9ed5-a45dd41393b1 · outbound
This paper cites an unresolved cited work.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Unresolved cited work
Reference 37
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T14:37:30.808723Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 134a7d3e-fdae-4e8c-a49e-982088d14ada · outbound
This paper cites Non-linear finite element analysis of solids and structures, volume 1: Essentials, ma crisfield, john wiley.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Non-linear finite element analysis of solids and structures, volume 1: Essentials, ma crisfield, john wiley
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:30.726527Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation efa1f3f8-1594-43ea-b410-ad558aaf48a2 · outbound
This paper cites Graph transformer networks.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Graph transformer networks
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:30.682398Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c7749e2a-c586-4d33-8752-fb6e7c6174c6 · outbound
This paper cites Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proceedings of the IEEE international conference on computer vision, pp.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proceedings of the IEEE international conference on computer vision, pp
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:30.637546Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 36e36c95-fa23-45a7-b76c-073925e9f880 · outbound
This paper cites Medical image analysis 17, 525–537.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Medical image analysis 17, 525–537
Reference 2013
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:31.576749Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c34ea42e-8a02-4b67-b2f3-34f6a7a978a1 · outbound
This paper cites Journal of Cardiovascular Magnetic Resonance 21, 62.
HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States Journal of Cardiovascular Magnetic Resonance 21, 62
Reference 2019
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:37:30.761089Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Pith citing papers
No inbound Pith citation observations are available.