Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2503.14911.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T15:18:45.769309Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-10T12:15:01.137692Z
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 1b61aaa9-76cf-41ff-9db0-539acd98ffeb · inbound
Grounding Multimodal Large Language Models with Quantitative Skin Attributes: A Retrieval Study cites this paper.
Grounding Multimodal Large Language Models with Quantitative Skin Attributes: A Retrieval Study Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T15:18:45.769309Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 391a2ebb-d737-4c24-9be4-2e0e7e382f34 · inbound
An End-to-End Deep Learning Framework for Arsenicosis Diagnosis Using Mobile-Captured Skin Images cites this paper.
An End-to-End Deep Learning Framework for Arsenicosis Diagnosis Using Mobile-Captured Skin Images Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T20:14:16.040602Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 93e632ad-4f67-4f8f-9c78-c12ea53abbd0 · inbound
DermAgent: A Self-Reflective Agentic System for Dermatological Image Analysis with Multi-Tool Reasoning and Traceable Decision-Making cites this paper.
DermAgent: A Self-Reflective Agentic System for Dermatological Image Analysis with Multi-Tool Reasoning and Traceable Decision-Making Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T02:58:33.453865Z
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.
Observation 3924da42-4c54-442d-800c-550c7abf2ca1 · inbound
APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms cites this paper.
APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology
Reference 272
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T06:14:19.447810Z
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.
Observation 1465edba-48ad-4e00-8c4c-31d7572166df · inbound
APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms cites this paper.
APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology
Reference 183
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T06:45:29.899541Z
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.