Paper Citation Record · LEDGER

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification

As of 22 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2607.26765.

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

pith.paper-citation-record.v1
2607.26765 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T21:46:17.051715Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

31 of 31 outbound references displayed

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verified fuzzy0
unresolved31
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malformed identifier0
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External citation measurements

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Outbound references

Observation 63c47572-bf1a-4445-8ec0-633085c2b66a · outbound

This paper cites Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040.JAMA Dermatol.2022,158, 495–503.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040.JAMA Dermatol.2022,158, 495–503

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.903515Z

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Observation 1e817078-370b-4d19-a1f7-0769b71cd5e7 · outbound

This paper cites Diagnostic Accuracy of Dermoscopy.Lancet Oncol.2002,3, 159–165.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Diagnostic Accuracy of Dermoscopy.Lancet Oncol.2002,3, 159–165

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.909452Z

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Observation e35ec6c1-e9c8-4267-877d-bdd76b320084 · outbound

This paper cites Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks.Nature2017,542, 115–118.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks.Nature2017,542, 115–118

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.914781Z

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Observation fa83ff53-44d5-403d-9d72-b356b4f710af · outbound

This paper cites an unresolved cited work.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.919623Z

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Observation 14625ab5-7d87-4394-a22c-09adb118d113 · outbound

This paper cites Shortcut Learning in Deep Neural Networks.Nat.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Shortcut Learning in Deep Neural Networks.Nat

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.925006Z

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Observation 0b17b9c8-7005-4d2b-a872-e9cf59235290 · outbound

This paper cites Uncovering and Correcting Shortcut Learning in Machine Learning Models for Skin Cancer Diagnosis.Diagnostics2022,12, 40.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Uncovering and Correcting Shortcut Learning in Machine Learning Models for Skin Cancer Diagnosis.Diagnostics2022,12, 40

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.929898Z

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Observation 4b54b1be-420d-42be-98c6-da4fc6d88b64 · outbound

This paper cites A Survey on Image Data Augmentation for Deep Learning.J.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification A Survey on Image Data Augmentation for Deep Learning.J

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.935145Z

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Observation 3a894424-8bc2-4fd0-96cd-c4f566fda5ef · outbound

This paper cites Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC).

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.940272Z

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Observation 9cfed614-873d-445b-96a5-c1847e91a68c · outbound

This paper cites an unresolved cited work.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.945340Z

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Observation 07c51525-c9c4-4156-86e9-fbba3149130d · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.950755Z

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Observation 410a8381-754c-4af2-9140-404787220837 · outbound

This paper cites The HAM10000 Dataset, a Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions.Sci.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification The HAM10000 Dataset, a Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions.Sci

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.955882Z

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Observation 403057a8-6649-4547-9009-ac8fc39f23d7 · outbound

This paper cites Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets.IEEE J.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets.IEEE J

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.960820Z

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Observation c28627c3-ae39-40aa-8917-8f108d920a43 · outbound

This paper cites Analysis of the ISIC Image Datasets: Usage, Benchmarks and Recommendations.Med.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Analysis of the ISIC Image Datasets: Usage, Benchmarks and Recommendations.Med

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.965887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a172ded6-9f84-43a8-a2e1-ec022e62d1a7 · outbound

This paper cites Data Augmentation for Skin Lesion Analysis.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Data Augmentation for Skin Lesion Analysis

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.970888Z

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Observation 94e51bcd-b201-440e-b127-9ad57be1ce9d · outbound

This paper cites Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis.Neurocomputing2020,383, 303–313.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis.Neurocomputing2020,383, 303–313

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.975420Z

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Observation 7d721356-b7ab-4fd3-be6b-58c6033d02ab · outbound

This paper cites RandAugment: Practical Automated Data Augmentation with a Reduced Search Space.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification RandAugment: Practical Automated Data Augmentation with a Reduced Search Space

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.980114Z

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Observation 53477013-7143-490d-b0f6-af79a4463342 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification mixup: Beyond Empirical Risk Minimization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.984912Z

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Observation 7a7f48fe-af13-45be-8380-deff5e88d5b7 · outbound

This paper cites (De)Constructing Bias on Skin Lesion Datasets.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification (De)Constructing Bias on Skin Lesion Datasets

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.989945Z

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Observation 94d4e296-6fce-49a1-aa6f-c7bda30efa0b · outbound

This paper cites Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set.Sci.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set.Sci

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.994521Z

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Observation 1af37baa-8901-47db-83db-b09024556d51 · outbound

This paper cites Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:16.999054Z

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Observation 2f3a3b8e-4f62-4068-b4a8-3aa86adbee9c · outbound

This paper cites Domain-Adversarial Training of Neural Networks.J.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Domain-Adversarial Training of Neural Networks.J

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:17.004143Z

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Observation 96460598-9fcc-4da5-a332-303b83a344b7 · outbound

This paper cites Tent: Fully Test-Time Adaptation by Entropy Minimization.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Tent: Fully Test-Time Adaptation by Entropy Minimization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:17.008799Z

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Observation b6d68ed2-728a-489c-bbe2-a619e49b8997 · outbound

This paper cites Visualizing Data Using t-SNE.J.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Visualizing Data Using t-SNE.J

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:17.013345Z

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Observation 49f17dc2-93be-4aeb-ad08-55d1c576ef41 · outbound

This paper cites A ConvNet for the 2020s.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification A ConvNet for the 2020s

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:17.018212Z

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Observation c21ceda8-b390-42fe-8598-1bd3f91f9e05 · outbound

This paper cites A Method for Normalizing Histology Slides for Quantitative Analysis.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification A Method for Normalizing Histology Slides for Quantitative Analysis

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:17.023522Z

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Observation 6a9a320f-db04-4181-9606-09e2b4da64e7 · outbound

This paper cites Quantifying the Effects of Data Augmentation and Stain Color Normalization in Convolutional Neural Networks for Computational Pathology.Med.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Quantifying the Effects of Data Augmentation and Stain Color Normalization in Convolutional Neural Networks for Computational Pathology.Med

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:17.028077Z

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Observation 70f3665e-e45f-44e2-8349-f26670785eeb · outbound

This paper cites AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:17.032584Z

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Observation 785d8bb7-8b22-4ad7-877d-3a27ac74d385 · outbound

This paper cites Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:17.037174Z

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Observation fa3254e0-7554-4bea-901e-4791211c0955 · outbound

This paper cites In Search of Lost Domain Generalization.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification In Search of Lost Domain Generalization

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:17.042765Z

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Observation 8d1ae475-de94-4f07-b84e-d51c58510aa9 · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification A Simple Framework for Contrastive Learning of Visual Representations

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:17.047359Z

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Observation c68f80a9-f03f-483d-a8b0-2a33d87c1d2b · outbound

This paper cites Test-Time Training with Self-Supervision for Generalization under Distribution Shifts.

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-30T21:46:17.051715Z

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Pith citing papers

No inbound Pith citation observations are available.