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Paper Citation Record · LEDGER

Learning to Segment Skin Lesions from Noisy Annotations

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1906.03815.

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

pith.paper-citation-record.v1
1906.03815 v2

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 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:46:03.695764Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T22:30:07.251860Z

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 3c4b3323-772c-41af-9f34-ffe3bd041449 · inbound

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation cites this paper.

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation Learning to Segment Skin Lesions from Noisy Annotations

Reference 90

Resolution
unresolved
no resolver link, observed 2026-08-14T10:46:03.695764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:46:03.695764Z digest=sha256:2ab7350aa3d6e7d2d4fb19b0b01a67187271c1c9209d3b5839c75829590e014e

Observation e6837ee7-7049-433f-b86d-9cc0b45899ea · inbound

HyperSORT: Self-Organising Robust Training with hyper-networks cites this paper.

HyperSORT: Self-Organising Robust Training with hyper-networks Learning to Segment Skin Lesions from Noisy Annotations

Reference 18

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T22:30:07.363953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T22:30:05.859405Z digest=sha256:a01a8edcc06e91a7423be201a8ba9a7c3671cf6a81a0257f3eaf20283d7c153f