Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning
This work addresses the challenge of making deep learning robust to incorrectly labeled data, which is critical for medical imaging tasks like skin lesion diagnosis, but it appears incremental as it builds on existing methods like MixUp and BalancedMix.
The paper tackles the noisy label problem in deep learning by proposing a method that uses test-time augmentation cross-entropy for label noise selection and NoiseMix learning for classifier training, achieving improved noise detection and classification performance on the ISIC-18 skin lesion dataset.
As the size of the dataset used in deep learning tasks increases, the noisy label problem, which is a task of making deep learning robust to the incorrectly labeled data, has become an important task. In this paper, we propose a method of learning noisy label data using the label noise selection with test-time augmentation (TTA) cross-entropy and classifier learning with the NoiseMix method. In the label noise selection, we propose TTA cross-entropy by measuring the cross-entropy to predict the test-time augmented training data. In the classifier learning, we propose the NoiseMix method based on MixUp and BalancedMix methods by mixing the samples from the noisy and the clean label data. In experiments on the ISIC-18 public skin lesion diagnosis dataset, the proposed TTA cross-entropy outperformed the conventional cross-entropy and the TTA uncertainty in detecting label noise data in the label noise selection process. Moreover, the proposed NoiseMix not only outperformed the state-of-the-art methods in the classification performance but also showed the most robustness to the label noise in the classifier learning.