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Skin lesion classification research benchmarks – Page 2 · SOTA2 Research

Skin lesion classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

91.82Macro F1

10

Apr 2, 2026

HAM10000 NID2 label quantity skew

46.17Accuracy

9

May 14, 2026

HAM10000 NID1_0.5 label/quantity skew

52.79Accuracy

9

May 14, 2026

HAM10000 NID1_0.2 label/quantity skew

50.23Accuracy

9

May 14, 2026

0.866AUC

9

May 1, 2026

soft voting ensemble of CNNs

96.32Accuracy

9

Feb 27, 2026

98.61Accuracy

8

Jun 19, 2026

Sechenov University

65.2Macro F1

8

Jun 12, 2026

83.6Macro F1

8

Jun 12, 2026

93.4Accuracy (CP)

8

Jun 4, 2026

HAM10000 (test)

0.97AUC

8

Feb 26, 2026

Hyperbolic prototype net

61.04Accuracy

8

Feb 26, 2026

PAD-UFES-20 (test)

79.8BCC

7

Jul 7, 2026

95.41Accuracy

7

Jun 19, 2026

Guided-tuning (GT)

79Accuracy

7

Feb 26, 2026

PAD-UFES-20 v1 (test)

CT-pretrain + GT

88Accuracy

7

Feb 26, 2026

ISIC R2=80% 2018 (test)

76.4Precision

6

Jul 8, 2026

ISIC R=80% 2018 (test)

81.3Precision

6

Jul 8, 2026

HAM10000 1,000-image subset 1.0

78.5Accuracy

4

Feb 26, 2026

FLamby ISIC2019 (repeat)

FENS iterative FedProx

75Balanced Acc.

3

Jul 3, 2026

0.18Energy Consumption (J/sample)

3

Mar 30, 2026

0.18Energy Efficiency (Lite, J/sample)

3

Mar 30, 2026

0.18Energy (J/sample) Lite

3

Mar 30, 2026

87Accuracy

3

Mar 5, 2026

86F1 Score

3

Mar 5, 2026

Page 2 of 3

Popular datasets

ISIC HAM10000 FedSkin Derm7pt PAD-UFES

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