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Paper Citation Record · LEDGER

Rethinking model prototyping through the MedMNIST+ dataset collection

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.15786.

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

pith.paper-citation-record.v1
2404.15786 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:56:56.070200Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T15:02:41.275651Z

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 00aec14f-59ee-4e56-8390-d3adf164b8ee · inbound

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST cites this paper.

Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST Rethinking model prototyping through the MedMNIST+ dataset collection

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-10T14:56:56.070200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T14:56:56.070200Z digest=sha256:b4d38f0e7c2bfdaf2e59768d927588baf10c4e0b6df707ccf56ac8d2f3379764

Observation 9ff5105f-f628-4ae7-a921-e3257ff4a7fe · inbound

LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training cites this paper.

LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training Rethinking model prototyping through the MedMNIST+ dataset collection

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-18T15:02:41.279008Z

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=2026-05-18T15:01:49.645065Z digest=sha256:b76490d543d3da16d7ecd5c96f4887033b95fdb08d64c8904da0546863032bf5