Paper Citation Record · LEDGER

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions

As of 21 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 1 inbound Pith citation observation for arXiv:2602.19857.

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

pith.paper-citation-record.v1
2602.19857 v2

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:32:58.594664Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:32:56.461213Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

verified exact0
verified fuzzy0
unresolved16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7d4bae7e-ae10-4368-a881-b72c0c590ea4 · outbound

This paper cites This gap remains largely from biases in image acquisition and dataset composition, which shape the visual features learned during training and can lead to unreliable predictions.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions This gap remains largely from biases in image acquisition and dataset composition, which shape the visual features learned during training and can lead to unreliable predictions

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:56.176910Z

Source-reported events for the cited work

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Observation 405a455c-0ab1-42eb-a681-114f67378c43 · outbound

This paper cites Ideally, f(x)should emulate the human decision,H(x)→y.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions Ideally, f(x)should emulate the human decision,H(x)→y

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:56.345516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b657cee-a997-470f-8539-8a220cea3a4d · outbound

This paper cites Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:56.461213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd0ab04b-1e6e-4f8b-9a27-02202540f7dc · outbound

This paper cites These models were optimized to classify in dermoscopic images.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions These models were optimized to classify in dermoscopic images

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:56.621809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8ae0d04-cc3c-4f2c-9ffb-2c909ea17852 · outbound

This paper cites Traditional machine learning approaches often struggle with domain variations, which critically impact real-world reliabil- ity.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions Traditional machine learning approaches often struggle with domain variations, which critically impact real-world reliabil- ity

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:56.833399Z

Source-reported events for the cited work

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Observation 9b2fcdf2-8bf5-44e1-afe5-7056867e0ddb · outbound

This paper cites Ethical approval was not required, as confirmed by the license attached to the open- access data.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions Ethical approval was not required, as confirmed by the license attached to the open- access data

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:57.039228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d5dddfd-aa88-498f-b535-d2d35a1876c5 · outbound

This paper cites an unresolved cited work.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:57.202483Z

Source-reported events for the cited work

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Observation 50c92f34-78c6-434c-b171-36792a4cd12c · outbound

This paper cites 8,248, Table 1.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions 8,248, Table 1

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:57.372262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf30a1df-de65-4d46-942a-87c0ea455ae4 · outbound

This paper cites Machine learning in dermatology: cur- rent applications, opportunities, and limitations,.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions Machine learning in dermatology: cur- rent applications, opportunities, and limitations,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:57.526322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0ec83af-1b7b-4347-a1c2-cb214a60254f · outbound

This paper cites Machine learning and health care disparities in dermatology,.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions Machine learning and health care disparities in dermatology,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:57.681505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54618cde-f06a-4a01-b512-c186df6945a8 · outbound

This paper cites An analysis of data variation and bias in image-based dermatological datasets for machine learning classification.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions An analysis of data variation and bias in image-based dermatological datasets for machine learning classification

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:57.790341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc354b15-40c6-4e42-a706-9d47736bfc4f · outbound

This paper cites Continual adaptation of visual representations via domain randomization and meta-learning,.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions Continual adaptation of visual representations via domain randomization and meta-learning,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:57.925869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9744718d-c5d5-421b-89f5-e0b130d0a986 · outbound

This paper cites PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones,.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:58.075285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5ccd3e2-96ab-4286-9964-5bd7ea3004f5 · outbound

This paper cites Disparities in dermatology AI perfor- mance on a diverse, curated clinical image set,.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions Disparities in dermatology AI perfor- mance on a diverse, curated clinical image set,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:58.283762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f46d8a64-f498-4c56-bc33-0d3b04a7daa1 · outbound

This paper cites Al- bumentations: fast and flexible image augmentations,.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions Al- bumentations: fast and flexible image augmentations,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:58.433839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 711c254b-4ec4-45d5-8084-5cf3ff095778 · outbound

This paper cites The ham10000 dataset, a large collection of multi-source dermato- scopic images of common pigmented skin lesions,.

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions The ham10000 dataset, a large collection of multi-source dermato- scopic images of common pigmented skin lesions,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:58.594664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 7b657cee-a997-470f-8539-8a220cea3a4d · inbound