Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning | Paper Citation Record

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This paper cites ViT-B16 processes input images by dividing them into 16×16 patches.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning ViT-B16 processes input images by dividing them into 16×16 patches

Reference 1

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Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Unresolved cited work

Reference 2

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This paper cites an unresolved cited work.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Unresolved cited work

Reference 3

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This paper cites an unresolved cited work.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Unresolved cited work

Reference 4

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This paper cites an unresolved cited work.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Unresolved cited work

Reference 5

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Observation 8daa9ed0-4e91-43df-bb47-5f4c86373581 · outbound

This paper cites • However, EOpp1 and EOdd showed significant group dif- ferences of approximately 7.4% and 8.2%, respectively, reflecting substantial fairness concerns.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning • However, EOpp1 and EOdd showed significant group dif- ferences of approximately 7.4% and 8.2%, respectively, reflecting substantial fairness concerns

Reference 6

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This paper cites an unresolved cited work.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Unresolved cited work

Reference 7

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This paper cites an unresolved cited work.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Unresolved cited work

Reference 8

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Observation 52fc4ab6-6444-40f0-9fce-19f6989a4847 · outbound

This paper cites A differentiable distance approximation for fairer image classification,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning A differentiable distance approximation for fairer image classification,

Reference 9

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Observation b8be6d62-11e3-4ba7-80cf-e4e95a6c9108 · outbound

This paper cites Furthermore, the authors extend their heartfelt gratitude to Dr.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Furthermore, the authors extend their heartfelt gratitude to Dr

Reference 10

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Observation 72b1a92a-11cd-460c-bc8f-ad766b54e363 · outbound

This paper cites Dermatologist-level classification of skin cancer with deep neural networks,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Dermatologist-level classification of skin cancer with deep neural networks,

Reference 11

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Observation 902d98e0-9b98-4fa4-b148-d27be87b696b · outbound

This paper cites Deep neural networks are superior to dermatologists in melanoma image classification,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Deep neural networks are superior to dermatologists in melanoma image classification,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:21.577832Z

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Observation 1a1c403c-0916-4241-9619-1ff96d641161 · outbound

This paper cites Ai-driven healthcare: Fairness in ai healthcare: A survey,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Ai-driven healthcare: Fairness in ai healthcare: A survey,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:21.383876Z

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This paper cites Fairness of artifi- cial intelligence in healthcare: review and recommendations,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Fairness of artifi- cial intelligence in healthcare: review and recommendations,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:21.183936Z

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Observation 41b3d2d6-a9f7-4510-9902-1e77c5fcab19 · outbound

This paper cites Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:19:15.442439Z

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Observation ca2a30f2-71e3-410b-8b57-5cfa3932a4b3 · outbound

This paper cites Skin type diversity in skin lesion datasets: A review,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Skin type diversity in skin lesion datasets: A review,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:21.068705Z

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Observation 1062bca4-b7f1-4360-b758-e11c11281ce2 · outbound

This paper cites What About Applied Fairness?.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning What About Applied Fairness?

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:19:15.250126Z

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Observation 3eecbef5-9dc4-4142-8e66-879a98c22365 · outbound

This paper cites Biasing & Debiasing based Approach Towards Fair Knowledge Transfer for Equitable Skin Analysis.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Biasing & Debiasing based Approach Towards Fair Knowledge Transfer for Equitable Skin Analysis

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:19:15.079044Z

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Observation 965d9a5f-c62b-4593-a5c6-9003ec65f866 · outbound

This paper cites Algorithmic fairness in lesion classification by mitigating class imbalance and skin tone bias,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Algorithmic fairness in lesion classification by mitigating class imbalance and skin tone bias,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:18.977248Z

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Observation a6bc55c0-da10-4ac6-a2a8-cf6d4f96141a · outbound

This paper cites Fairdisco: Fairer ai in dermatology via disentanglement contrastive learning,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Fairdisco: Fairer ai in dermatology via disentanglement contrastive learning,

Reference 20

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Observation 07d661e2-a527-4c91-9cd7-8d50960abef7 · outbound

This paper cites Skin deep: Investigating subjectivity in skin tone annotations for computer vision benchmark datasets,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Skin deep: Investigating subjectivity in skin tone annotations for computer vision benchmark datasets,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:20.569451Z

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Observation aef96788-b7a8-43ff-ad6c-80be807f7a4f · outbound

This paper cites What would the outputs be if the skin tone were lighter?.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning What would the outputs be if the skin tone were lighter?

Reference 22

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Observation 43b641e3-9c4f-4052-bac3-97da866438b1 · outbound

This paper cites Which skin tone measures are the most inclusive? an investigation of skin tone measures for artificial intelligence,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Which skin tone measures are the most inclusive? an investigation of skin tone measures for artificial intelligence,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:20.363340Z

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Observation 7d40a6fa-fac6-42ba-9089-8d0fa019c138 · outbound

This paper cites The validity and practicality of sun-reactive skin types i through vi,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning The validity and practicality of sun-reactive skin types i through vi,

Reference 24

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Observation 50ca4711-29a2-4338-baac-92e73d9c5dc7 · outbound

This paper cites Developing the monk skin tone scale,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Developing the monk skin tone scale,

Reference 25

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This paper cites Expert in skin and hair types around the world,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Expert in skin and hair types around the world,

Reference 26

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Observation 0f816169-468d-4fd0-925f-9837206586ec · outbound

This paper cites Towards transparency in dermatology image datasets with skin tone annotations by experts, crowds, and an algorithm,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Towards transparency in dermatology image datasets with skin tone annotations by experts, crowds, and an algorithm,

Reference 27

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Observation 0a14c920-f19f-4925-93a0-0ad46a3b9e3e · outbound

This paper cites Understanding racial and ethnic differences in health in late life: A research agenda,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Understanding racial and ethnic differences in health in late life: A research agenda,

Reference 28

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Observation dab692da-b525-43d0-aad0-c3d64cc1fbfb · outbound

This paper cites A web-based mpox skin lesion detection system using state-of-the-art deep learning models considering racial diversity,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning A web-based mpox skin lesion detection system using state-of-the-art deep learning models considering racial diversity,

Reference 29

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Observation ff716ad7-c7fa-40f7-b283-eca5c06d5b59 · outbound

This paper cites EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation

Reference 30

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Observation 67a9dec0-c797-444e-bff1-f1257ab19a8c · outbound

This paper cites Assessing bias in skin lesion classifiers with contemporary deep learning and post-hoc explainability techniques,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Assessing bias in skin lesion classifiers with contemporary deep learning and post-hoc explainability techniques,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:18.774025Z

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This paper cites Circle: Color invariant representation learning for unbiased classification of skin lesions,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Circle: Color invariant representation learning for unbiased classification of skin lesions,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:18.569614Z

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Observation c56ab28d-4f0a-4a77-85ea-a02d8c6a03da · outbound

This paper cites Evaluating and mitigating bias in image classifiers: A causal perspective using coun- terfactuals,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Evaluating and mitigating bias in image classifiers: A causal perspective using coun- terfactuals,

Reference 33

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Observation 66eab699-1020-4602-824d-e362e1d044c2 · outbound

This paper cites Detecting melanoma fairly: Skin tone detection and debiasing for skin lesion classification,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Detecting melanoma fairly: Skin tone detection and debiasing for skin lesion classification,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:18.111638Z

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Observation 6b76d973-878c-4d03-8a67-bf78a72bc97d · outbound

This paper cites Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings,

Reference 35

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Observation c7eac87d-5767-4aa4-b4c9-2ea8e519c5dd · outbound

This paper cites Learning not to learn: Training deep neural networks with biased data,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Learning not to learn: Training deep neural networks with biased data,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:17.752480Z

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Observation 3cabcaa5-aea5-4040-acda-eff546bca31b · outbound

This paper cites Estimating and Improving Fairness with Adversarial Learning.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Estimating and Improving Fairness with Adversarial Learning

Reference 37

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Observation 87be99f5-882c-437f-9eaa-38d3046c5309 · outbound

This paper cites Exploit- ing transferable knowledge for fairness-aware image classification,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Exploit- ing transferable knowledge for fairness-aware image classification,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:17.585057Z

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Observation 7e8e6ab4-2f3f-4740-a2f7-e18af1e2c4b2 · outbound

This paper cites Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:19:14.590450Z

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This paper cites Achieving flexible fairness metrics in federated medical imaging,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Achieving flexible fairness metrics in federated medical imaging,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:17.413382Z

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Observation b2999b2f-25b0-4964-8c28-7c065ae99f9d · outbound

This paper cites Fairprune: Achieving fairness through pruning for dermatological disease diagnosis,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Fairprune: Achieving fairness through pruning for dermatological disease diagnosis,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:17.238926Z

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Observation 690a8826-5c54-4b3e-b9f0-7eef6ba9b925 · outbound

This paper cites Toward fairness through fair multi-exit framework for dermatological disease diagnosis,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Toward fairness through fair multi-exit framework for dermatological disease diagnosis,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:17.062578Z

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Observation 5183c782-4d02-475f-a379-fc5433ede88e · outbound

This paper cites Achieving fairness through channel pruning for dermatological disease diagnosis,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Achieving fairness through channel pruning for dermatological disease diagnosis,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:16.879331Z

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Observation eb0b915b-67a0-4544-ae82-e4de9e3b84cf · outbound

This paper cites Fairquantize: Achieving fairness through weight quantization for dermatological disease diagnosis,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Fairquantize: Achieving fairness through weight quantization for dermatological disease diagnosis,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:16.720543Z

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Observation ca4b068d-de7b-437a-ab5c-4cc1a06e909a · outbound

This paper cites A Fair Loss Function for Network Pruning.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning A Fair Loss Function for Network Pruning

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:19:14.416807Z

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Observation be2749e0-65c9-4440-87b1-0bb1108ff10e · outbound

This paper cites Fairgrape: Fairness-aware gradient pruning method for face attribute classification,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Fairgrape: Fairness-aware gradient pruning method for face attribute classification,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:16.580023Z

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Observation 1c54434e-4c19-4fc0-b36e-554bc55f7677 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Imagenet: A large-scale hierarchical image database,

Reference 47

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Source-reported events for the cited work

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This paper cites Domain generalization for medical image analysis: A review,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Domain generalization for medical image analysis: A review,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:16.405777Z

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Observation d5293f85-f9c2-43aa-a279-56750f106717 · outbound

This paper cites Domain adaptation for medical image analysis: a survey,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Domain adaptation for medical image analysis: a survey,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:16.232820Z

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This paper cites Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations

Reference 50

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This paper cites Intriguing properties of vision transformers,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Intriguing properties of vision transformers,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:16.066272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9d367f73-f40a-403e-9ee9-ad42a817a671 · outbound

This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T13:19:13.751785Z

Source-reported events for the cited work

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Observation 7adf5ee2-2395-40db-b984-336ee6e65242 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-05T13:19:13.842093Z

Source-reported events for the cited work

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Observation 3988ad41-51e1-4433-a976-c671ae2774bf · outbound

This paper cites Attention is all you need,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Attention is all you need,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T13:19:13.938879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9512b3b7-1501-4bf9-9111-94dba0d15257 · outbound

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

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:15.885522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fb5d7d1e-b511-4138-b9ce-3d106736296d · outbound

This paper cites an unresolved cited work.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:19:15.725432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9c176a9d-5e75-440f-ba1b-67233731d750 · outbound

This paper cites Bcn20000: Dermoscopic lesions in the wild,.

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Bcn20000: Dermoscopic lesions in the wild,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:15.578445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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