Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Unsupervised Brain Anomaly Detection and Segmentation with Transformers
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2102.11650.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-18T09:07:19.408341Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T09:11:09.883613Z
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 4a8219c7-11b6-42c2-8542-fd53c16abc6a · inbound
RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scans cites this paper.
RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scans Unsupervised Brain Anomaly Detection and Segmentation with Transformers
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T09:11:09.886204Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 26f95984-b28f-4152-9250-15de561dd841 · inbound
Scaling Pretrained Representations Enables Label-Free Out-of-Distribution Detection Without Fine-Tuning cites this paper.
Scaling Pretrained Representations Enables Label-Free Out-of-Distribution Detection Without Fine-Tuning Unsupervised Brain Anomaly Detection and Segmentation with Transformers
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T18:36:09.001274Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.