Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T13:53:49.508467Z
Paper Citation Record · LEDGER
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs
As of 16 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:1908.04181.
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 2026-08-16T06:30:59.297886+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
17 of 17 outbound references displayed
- verified exact0
- verified fuzzy17
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 80646667-d578-47bf-9d55-b0b6cd9df4f0 · outbound
This paper cites (2016) A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2016) A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.863228Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fe40a19c-d1f4-4632-be47-9e7f0bfccc01 · outbound
This paper cites (2019) Skin Lesion Classification Using CNNs with Patch- Based Attention and Diagnosis-Guided Loss Weighting.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2019) Skin Lesion Classification Using CNNs with Patch- Based Attention and Diagnosis-Guided Loss Weighting
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.847757Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5fecfbf1-7b75-4efe-b3f4-62ea9a6374a4 · outbound
This paper cites (2016) Deep residual learning for image recog- nition.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2016) Deep residual learning for image recog- nition
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.821843Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 36184791-f12b-4eb5-a63e-cf01994ae5b0 · outbound
This paper cites (2018) Squeeze-and-excitation networks.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2018) Squeeze-and-excitation networks
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.800563Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 975de0e7-4332-4184-a020-5d8e4c2194e5 · outbound
This paper cites (2017) Densely con- nected convolutional networks.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2017) Densely con- nected convolutional networks
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.776203Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3f0225f6-2d03-4d01-9155-4e8ab3215b60 · outbound
This paper cites (2018) Full Quantification of Left Ventri- cle Using Deep Multitask Network with Combination of 2D and 3D Convolution on 2D+ t Cine MRI.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2018) Full Quantification of Left Ventri- cle Using Deep Multitask Network with Combination of 2D and 3D Convolution on 2D+ t Cine MRI
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.760743Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3163c2b3-c121-4b79-ae44-0c4ebd6adae1 · outbound
This paper cites (2009) The role of cardiovascular magnetic resonance imaging in heart failure.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2009) The role of cardiovascular magnetic resonance imaging in heart failure
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.744781Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5a4fd04a-1e7a-416f-9208-000715dbef19 · outbound
This paper cites (2018) Left Ventricle Full Quantification using Deep Layer Aggre- gation based Multitask Relationship Learning.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2018) Left Ventricle Full Quantification using Deep Layer Aggre- gation based Multitask Relationship Learning
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.729641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 99ae763b-9e22-4aae-b1ee-c1596843ba2b · outbound
This paper cites Gessert and M.
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.711049Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e8eb261b-37ed-4598-92c6-d13390231328 · outbound
This paper cites (2017) Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2017) Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.686860Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation eafc1c0e-b273-4887-8724-10a710d91e81 · outbound
This paper cites (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.674109Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation bc56d238-61c6-46f4-a0f4-65da07663187 · outbound
This paper cites (2015) Quantification of LV function and mass by cardiovascular magnetic reso- nance: multi-center variability and consensus contours.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2015) Quantification of LV function and mass by cardiovascular magnetic reso- nance: multi-center variability and consensus contours
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.658360Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d1131324-3e1f-450f-9491-b8900718dc06 · outbound
This paper cites (2019) Quantification of Full Left Ventricular metrics via Deep Regression Learning with Contour-guidance.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2019) Quantification of Full Left Ventricular metrics via Deep Regression Learning with Contour-guidance
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.640813Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9fc83da2-8a2a-49ca-9be8-8613e87e8313 · outbound
This paper cites (2017) Aggregated residual trans- formations for deep neural networks.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2017) Aggregated residual trans- formations for deep neural networks
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.625732Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5c3213fc-09cb-42b7-9e03-bf5565d32893 · outbound
This paper cites (2018) Calculation of Anatomical and Functional Metrics Using Deep Learning in Cardiac MRI: Comparison Between Direct and Segmentation-Based Estimation.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2018) Calculation of Anatomical and Functional Metrics Using Deep Learning in Cardiac MRI: Comparison Between Direct and Segmentation-Based Estimation
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.609610Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 82e51a51-2fc6-4247-a255-3fa72486d459 · outbound
This paper cites (2018) Full left ventricle quan- tification via deep multitask relationships learning.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2018) Full left ventricle quan- tification via deep multitask relationships learning
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.586820Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e34c6c64-3d2c-4382-a286-020c8c3fa53b · outbound
This paper cites (2017) Full quantification of left ventricle via deep multitask learning network respecting intra- and inter-task relatedness.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs (2017) Full quantification of left ventricle via deep multitask learning network respecting intra- and inter-task relatedness
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:53:49.565253Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.