Pith. sign in

Paper Citation Record · LEDGER

Exploring large scale public medical image datasets

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1907.12720.

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

pith.paper-citation-record.v1
1907.12720 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:27:22.433551Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:05:56.244632Z

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 b7f6359d-f5c3-4362-82a2-7fd6a68fb5e9 · inbound

Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification cites this paper.

Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification Exploring large scale public medical image datasets

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-14T05:27:22.433551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T05:27:22.433551Z digest=sha256:b021add8c66c955284c915476c7f6a60976884663b64e3c80c4e4989c0cd450a

Observation 570f0386-782f-4c59-bb6b-bb97e6990e8d · inbound

Faithful, Interpretable Chest X-ray Diagnosis with Anti-Aliased B-cos Networks cites this paper.

Faithful, Interpretable Chest X-ray Diagnosis with Anti-Aliased B-cos Networks Exploring large scale public medical image datasets

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-06T15:05:56.247852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T15:05:55.967186Z digest=sha256:9678390013af2774f91d46bbd4872cc7f7a99277d07522fb5181599cd0f73a92

Observation b432e8b0-4020-424d-a4a0-3cb6447bf5af · inbound

Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification cites this paper.

Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification Exploring large scale public medical image datasets

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-04T13:23:46.085099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T13:23:46.085099Z digest=sha256:169747127a778e3d1245ca6c8551311e23b0e106abbf737d5ba0fb5c6503be14