Paper Citation Record · LEDGER

MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.17536.

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

pith.paper-citation-record.v1
2406.17536 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:21:17.967185Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T20:26:12.448781Z

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 33df9c7a-f727-447c-b8b0-3f4c76bdfce1 · inbound

On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable? cites this paper.

On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable? MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:17.967185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf9e0457-e387-407e-a6f2-48b15222b193 · inbound

Beyond Overconfidence: Foundation Models Redefine Calibration in Deep Neural Networks cites this paper.

Beyond Overconfidence: Foundation Models Redefine Calibration in Deep Neural Networks MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T04:50:01.358097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9719de73-8bbf-469c-a49f-7e82e7349b0e · inbound

An autonomous agent for auditing and improving the reliability of clinical AI models cites this paper.

An autonomous agent for auditing and improving the reliability of clinical AI models MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:23:49.278347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7433926d-244e-4a7b-8674-cb2eeffc3df9 · inbound

Extending ZACH-ViT to Robust Medical Imaging: Corruption and Adversarial Stress Testing in Low-Data Regimes cites this paper.

Extending ZACH-ViT to Robust Medical Imaging: Corruption and Adversarial Stress Testing in Low-Data Regimes MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:40:49.996594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6d78381f-673e-4c44-85b6-841f416a58e8 · inbound

Useful nonrobust features are ubiquitous in biomedical images cites this paper.

Useful nonrobust features are ubiquitous in biomedical images MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:26:12.452863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1136d190-056c-4027-b9c8-87e368e9abbe · inbound

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification cites this paper.

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T03:20:32.855431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.