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Paper Citation Record · LEDGER

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification

As of 10 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2506.18414.

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

pith.paper-citation-record.v1
2506.18414 v3

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:20:06.280520Z

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

10 of 10 outbound references displayed

  • verified exact0
  • verified fuzzy4
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 57933fff-3752-468f-9a69-f0fead639e19 · outbound

This paper cites Trends in malignant melanoma mortality in 31 countries from 1985 to 2015.British Journal of Dermatology, 183(6):1056–1064, 2020.

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification Trends in malignant melanoma mortality in 31 countries from 1985 to 2015.British Journal of Dermatology, 183(6):1056–1064, 2020

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:07.164698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:20:05.344865Z digest=sha256:67fa7cd4c7f2767b04d4e3056a162f4305ad08cf7d403db000bf125e7bb96676

Observation ab1328bb-f77d-42b3-bbc5-835e946041f0 · outbound

This paper cites Classification of melanoma and nevus in digital images for diagnosis of skin cancer.IEEE Access, 7:90132–90144, 2019.

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification Classification of melanoma and nevus in digital images for diagnosis of skin cancer.IEEE Access, 7:90132–90144, 2019

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:06.903053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:20:05.402479Z digest=sha256:f415a620754b33182e17c7df1d6219dc6c60d7d247b73206d887b92a830cb18a

Observation 79e920cc-3793-41d4-a632-d7ee8930d8df · outbound

This paper cites Auto-encoding variational bayes, 2013.

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification Auto-encoding variational bayes, 2013

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:05.517590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:05.517590Z digest=sha256:ee97707dd9a0db61ee96c88ed2433c2c322b60873e5c31187aeb008bff5b1b13

Observation 88bec377-269e-452e-996f-8ecadd098eb7 · outbound

This paper cites Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders.

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:06.766382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:20:05.602953Z digest=sha256:3f60c9893ca14c5d85b7ec3e95b46ef13338af11f4699b05edc1ec729f9a225b

Observation 664cc45f-5895-4aa9-9a0d-5a2a923bd444 · outbound

This paper cites Anomaly Detection for Skin Disease Images Using Variational Autoencoder.

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification Anomaly Detection for Skin Disease Images Using Variational Autoencoder

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:05.733125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:05.733125Z digest=sha256:9a7bb34551ebe94615dfad559a5ee0334cf7de7c63f59d83d50a748e74418196

Observation 3a552ff5-f0f5-4f0b-8a2a-92d798fb6769 · outbound

This paper cites Sensitivity analysis of latent variables in variational autoencoders for dermoscopic image analysis.

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification Sensitivity analysis of latent variables in variational autoencoders for dermoscopic image analysis

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:20:06.569229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T23:20:05.824180Z digest=sha256:881315e536aa045813b0da7def2c773d78d9b380b6e01c5d585635738ea771f0

Observation 3375c1eb-6b4f-4552-a9c3-290bacbf75d5 · outbound

This paper cites Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study.

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:05.916050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:05.916050Z digest=sha256:b5984f4d155d116e14d279aa9ccc80066244775fe3cc772fba4efa8ff0819a7d

Observation 51ece513-aab8-4bc7-9b6b-fe9348170075 · outbound

This paper cites The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.Scientific data, 5(1):1–9, 2018.

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.Scientific data, 5(1):1–9, 2018

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:06.043551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:06.043551Z digest=sha256:484f6ebc7b0db117f2c681d1951b5e0e62331a4f8a97f7b727722f4935426fd6

Observation cd8ee904-86e6-4255-9fa7-3a57af63a2f2 · outbound

This paper cites an unresolved cited work.

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification Unresolved cited work

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:06.181393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:06.181393Z digest=sha256:3e5b4534693cbe89380c985f0f6ff554cf8f0819a2d20ba5fc362d9cccd33fe7

Observation 99840f5c-aaf0-4c1a-9447-ce22e09b1c8e · outbound

This paper cites BCN20000: Dermoscopic Lesions in the Wild.

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification BCN20000: Dermoscopic Lesions in the Wild

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:06.280520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:06.280520Z digest=sha256:c86c7873ec68ec388f8dea98fcbeb49c5ccf73a5233a7628c5b992e984035658

Pith citing papers

No inbound Pith citation observations are available.