Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.01034.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T17:39:15.137191Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T18:24:55.927180Z
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 9ec821b7-8613-4cc5-b343-d1e9330f7841 · inbound
Comparative Analysis of Pre-trained Deep Learning Models and DINOv2 for Cushing's Syndrome Diagnosis in Facial Analysis cites this paper.
Comparative Analysis of Pre-trained Deep Learning Models and DINOv2 for Cushing's Syndrome Diagnosis in Facial Analysis VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:39:15.137191Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 58ee4bd3-8a82-4d87-b660-153f2488e537 · inbound
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement cites this paper.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T18:24:55.954784Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.