Paper Citation Record · LEDGER

A Survey on Deep Learning of Small Sample in Biomedical Image Analysis

As of 16 August 2026, this Paper Citation Record lists 5 of 5 outbound references and 2 inbound Pith citation observations for arXiv:1908.00473.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.00473 v1

Coverage vector

measured 5 of 5 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:55:59.330368Z

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:46:03.858338Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-14T10:46:03.994828Z

Reference resolution

5 of 5 outbound references displayed

verified exact1
verified fuzzy0
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a2080409-221b-454c-a96f-14a3704f7204 · outbound

This paper cites an unresolved cited work.

A Survey on Deep Learning of Small Sample in Biomedical Image Analysis Unresolved cited work

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-14T15:55:59.330368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a32231a-e780-40d2-88e7-0474ea9793bc · outbound

This paper cites The Effectiveness of Data Augmentation in Image Classification using Deep Learning.

A Survey on Deep Learning of Small Sample in Biomedical Image Analysis The Effectiveness of Data Augmentation in Image Classification using Deep Learning

Reference 191

Resolution: unresolved; no resolver link, observed 2026-08-14T15:55:59.323598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb96dd9b-d2a6-4ac1-b97b-c6b60115b893 · outbound

This paper cites Unsupervised Domain Adaptation in Brain Lesion Segmentation with Adversarial Networks, in: Niethammer, M., Styner, M., Aylward, S., Zhu, H., Oguz, I., Yap, P.

A Survey on Deep Learning of Small Sample in Biomedical Image Analysis Unsupervised Domain Adaptation in Brain Lesion Segmentation with Adversarial Networks, in: Niethammer, M., Styner, M., Aylward, S., Zhu, H., Oguz, I., Yap, P

Reference 2600

Resolution: unresolved; no resolver link, observed 2026-08-14T15:55:59.318560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3d0ef2c-7e8c-41fc-aedc-77afd51d81c7 · outbound

This paper cites Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation.

A Survey on Deep Learning of Small Sample in Biomedical Image Analysis Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation

Reference 2854

Resolution: unresolved; no resolver link, observed 2026-08-14T15:55:59.307988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0af0d724-6b13-401f-98e1-96e3e809fa0c · outbound

This paper cites Adversarial synthesis learning enables segmentation without target modality ground truth, in: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

A Survey on Deep Learning of Small Sample in Biomedical Image Analysis Adversarial synthesis learning enables segmentation without target modality ground truth, in: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

Reference 7023

Resolution: verified exact; raw_fallback, observed 2026-08-14T15:55:59.591089Z

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

Observation 9e5d527d-f2a4-4829-a20d-e3b298ad8f6e · inbound

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation cites this paper.

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation A Survey on Deep Learning of Small Sample in Biomedical Image Analysis

Reference 147

Resolution: verified exact; local_arxiv, observed 2026-08-14T10:46:03.998222Z

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 d0d4d863-61c4-4fa1-97e1-58e40c00511d · inbound

Beyond Random Partitioning: Unsupervised Spatio-Temporal Stratification for Cohort Balancing in Longitudinal Medical Imaging cites this paper.

Beyond Random Partitioning: Unsupervised Spatio-Temporal Stratification for Cohort Balancing in Longitudinal Medical Imaging A Survey on Deep Learning of Small Sample in Biomedical Image Analysis

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T01:27:31.126611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.