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Paper Citation Record · LEDGER

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification

As of 8 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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:05.344865Z digest=sha256:a0ae814b592f132e1e81a4a751923bfb3933d1fc715aa89eefeca7472cc4cf00

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:05.402479Z digest=sha256:4709ea8e38d3116cfb7fddf3fce6edd6a3d47c98b3c57ef9e3692a91df017f61

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=sha256:651fa802a5608cfe7aa584ef9a450c88a33b33795b5952ec10b2b4b0a0387c99

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:05.602953Z digest=sha256:78716bbb5d3b59f41aef5eaa57ef81f2f9dc1eaa57bf5dfa2fe06c93b0b9575d

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:c9cd07023406190269f27ac85de566f369df7d8650dac20a99175e8acfb43bc0

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:20:05.824180Z digest=sha256:e9d008a0f25af92dab8e92647a613b454ddb9400cb38875feec120e9e8c8d404

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=sha256:62a20483dee0b1d3050e33d4d48ba7258bbd79fa6027cd2c6a760908381d8321

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:a6fbf1e6c2b6a74aec0fc7aeeb160ad0d8f9bbbe9b3792163ad6db5b9fe4710b

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=sha256:9ea65379bbe95c9569a19dc35f322fc5e9476afe31c8905e6f13b184c8b7fe7b

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:a123996eb2b6625d43b98cd3c14b2ab896b0df4af008482e19a7615a2d0a26a6

Pith citing papers

No inbound Pith citation observations are available.