A new self-supervised method using chaotic image transformations and attentive feature fusion achieves accuracies of 0.9221 on ISIC 2018 and 0.8644 on APTOS 2019.
Computer Modeling in Engineering & Sciences (CMES)140(1) (2024)
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Attention-Based Chaotic Self-Supervision for Medical Image Classification
A new self-supervised method using chaotic image transformations and attentive feature fusion achieves accuracies of 0.9221 on ISIC 2018 and 0.8644 on APTOS 2019.