Paper Citation Record · LEDGER

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification

As of 21 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.12987.

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

pith.paper-citation-record.v1
2607.12987 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T06:13:35.531796Z

measured 27 of 27 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 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

27 of 27 outbound references displayed

verified exact0
verified fuzzy0
unresolved24
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 41dbd2a5-5824-4bb6-90c9-6fef1bb3036b · outbound

This paper cites arXiv (Cornell University) (2024).https://doi.org/10.48550/arxiv.2409.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification arXiv (Cornell University) (2024).https://doi.org/10.48550/arxiv.2409

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.428926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2a6d08a-2858-40a8-8697-e3d5f1b41b93 · outbound

This paper cites In: Deep Generative Models.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification In: Deep Generative Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.433588Z

Source-reported events for the cited work

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Observation 91c653f8-8a12-4e75-ba30-67d05f2e614b · outbound

This paper cites Journal of Clinical Oncology27(36), 6199– 6206 (11 2009).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification Journal of Clinical Oncology27(36), 6199– 6206 (11 2009)

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.438622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1614e7b-a84d-4d70-933c-fabfc84d7f5c · outbound

This paper cites International Conference on Learning Representations (2018).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification International Conference on Learning Representations (2018)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.442491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4177f398-cd93-4118-a814-ff3bc3dab588 · outbound

This paper cites an unresolved cited work.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.446897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00a4a56a-50af-433e-8fe0-2ca0023218b2 · outbound

This paper cites FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy Classification.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy Classification

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-08-02T06:18:46.525676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 06eeec56-ee90-4e44-952b-02e0c59e17a6 · outbound

This paper cites Telemedicine and e-Health25, 1022–1032 (11 2019) 10 Carrión et al.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification Telemedicine and e-Health25, 1022–1032 (11 2019) 10 Carrión et al

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.455694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6affa155-8079-4ae8-9e23-ecffb02d8008 · outbound

This paper cites an unresolved cited work.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.459752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0089668f-11a1-4a0a-82f4-07b47d74f29e · outbound

This paper cites JAMA Dermatology157 (09 2021).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification JAMA Dermatology157 (09 2021)

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.463094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c12efd3-5fe1-4a15-8c50-fa99d6be956d · outbound

This paper cites Science Advances8(08 2022).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification Science Advances8(08 2022)

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.466283Z

Source-reported events for the cited work

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Observation 277e5fda-4285-4a24-be5c-d2341a1c3414 · outbound

This paper cites NIPS (06 2021).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification NIPS (06 2021)

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.470607Z

Source-reported events for the cited work

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Observation 5aebafe5-af6d-4f17-9be4-9d091b24cdd4 · outbound

This paper cites Lecture Notes in Computer Science13804, 185–202 (2023).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification Lecture Notes in Computer Science13804, 185–202 (2023)

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.474300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e078b02c-ab08-4e89-b043-be5b51eb025d · outbound

This paper cites In: The Eleventh International Conference on Learning Representations (2023).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification In: The Eleventh International Conference on Learning Representations (2023)

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.477896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17769def-f2e3-496d-b598-d0a242d67564 · outbound

This paper cites Nature Medicine30(2), 573–583 (2 2024).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification Nature Medicine30(2), 573–583 (2 2024)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.481404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51fcb5c1-1036-4fc2-8891-2380b19f0ca8 · outbound

This paper cites CVPRW (04 2021).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification CVPRW (04 2021)

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.484825Z

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Observation 3e3efd6c-50ae-45b8-871d-fdfcbcd91297 · outbound

This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.488257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99759689-570b-45e6-b7ec-17febe6f57a3 · outbound

This paper cites In: International Conference on Learning Representations (2022).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification In: International Conference on Learning Representations (2022)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.492100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3bbbcda-79e4-42df-93b5-03ecbc891adb · outbound

This paper cites Nature Medicine30(4), 1166–1173 (4 2024).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification Nature Medicine30(4), 1166–1173 (4 2024)

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.495667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c756ae96-4635-49e3-973f-781e8c783d1d · outbound

This paper cites Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.499109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25368997-9365-4048-ace2-a6b5726194cc · outbound

This paper cites 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.503051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db735edd-155b-4eb6-8ae8-f544f8eb545c · outbound

This paper cites 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.508019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8d7bd70-d421-4f7e-bd3f-94ac7e5560d2 · outbound

This paper cites Augmenting medical image classifiers with synthetic data from latent diffusion models.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification Augmenting medical image classifiers with synthetic data from latent diffusion models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.511871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5f7ef91-eabf-4863-a2b0-104a5c445abc · outbound

This paper cites In: NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research (2022).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification In: NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research (2022)

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.516222Z

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Unavailable: canonical work link unavailable.

Observation 00ce64ac-abb6-41e5-b18f-193e9dd86aa2 · outbound

This paper cites Scientific Data5(08 2018) cgDDI 11.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification Scientific Data5(08 2018) cgDDI 11

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.519545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4257d42a-3164-4e81-99ac-b819dbaa168b · outbound

This paper cites From Majority to Minority: A Diffusion-based Augmentation for Underrepresented Groups in Skin Lesion Analysis.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification From Majority to Minority: A Diffusion-based Augmentation for Underrepresented Groups in Skin Lesion Analysis

Reference 25

Resolution: metadata mismatch; local_arxiv, observed 2026-08-02T06:18:46.231678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 859c30c3-d836-47f2-a2eb-b07604e295e3 · outbound

This paper cites An Improved Method for Personalizing Diffusion Models.

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification An Improved Method for Personalizing Diffusion Models

Reference 26

Resolution: metadata mismatch; local_arxiv, observed 2026-08-02T06:18:45.941837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation df93f069-79aa-40cd-8b41-08403697a1bd · outbound

This paper cites In: CVPR (2018).

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification In: CVPR (2018)

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:35.531796Z

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Pith citing papers

No inbound Pith citation observations are available.