Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1910.04597.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:00:28.076520Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-12T08:41:25.244785Z
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 21f24d05-09e2-4d5b-81f4-1b81d2b07369 · inbound
Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation in Multiple Sclerosis cites this paper.
Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation in Multiple Sclerosis Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:00:28.076520Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3718925c-13f1-4be8-b43a-4d6e7711b943 · inbound
Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation cites this paper.
Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T10:56:51.427861Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6590dbb8-e89e-4908-a345-fc2c2a131c33 · inbound
Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment cites this paper.
Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:41:25.247625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 32839c0f-0a85-4454-8062-1e16e8b176fc · inbound
Robustness of Transformer-Based Fluence Map Prediction Under Clinically Realistic Perturbations cites this paper.
Robustness of Transformer-Based Fluence Map Prediction Under Clinically Realistic Perturbations Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T15:26:19.725634Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation a377cd30-5722-4fb2-922c-c1c692e82722 · inbound
Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection cites this paper.
Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects
Reference 29
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T17:01:09.311727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.