Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1709.07330.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T15:30:41.242543Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-24T15:14:37.167902Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 2149b3e2-3092-4622-817c-560af1ae577e · inbound
Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation cites this paper.
Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T15:14:37.171207Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 51173df4-b676-4a7f-b3a6-b027dfb099f1 · inbound
Adaloss: Adaptive Loss Function for Landmark Localization cites this paper.
Adaloss: Adaptive Loss Function for Landmark Localization H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T15:30:41.242543Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0e4b5d4a-7ce3-4e6a-a479-8dd56d84036f · inbound
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images cites this paper.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:59:01.615287Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.