SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss
<|reference_start|>SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss: Fundus image classification is crucial in the computer aided diagnosis tasks, but label noise significantly impairs the performance of deep neural networks. To address this challenge, we propose a robust framework, Self-Supervised Pre-training with Robust Adaptive Credal Loss (SSP-RACL), for handling label noise in fundus image datasets. First, we use Masked Autoencoders (MAE) for pre-training to extract features, unaffected by label noise. Subsequently, RACL employ a superset learning framework, setting confidence thresholds and adaptive label relaxation parameter to construct possibility distributions and provide more reliable ground-truth estimates, thus effectively suppressing the memorization effect. Additionally, we introduce clinical knowledge-based asymmetric noise generation to simulate real-world noisy fundus image datasets. Experimental results demonstrate that our proposed method outperforms existing approaches in handling label noise, showing superior performance.<|reference_end|>
@article{ye2024ssp-racl:,
    title={SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised
  Pretraining and Robust Adaptive Credal Loss},
    author={Mengwen Ye, Yingzi Huangfu, You Li, Zekuan Yu},
    journal={arXiv preprint arXiv:2409.18147},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18147},
    primaryClass={cs.CV}
}