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Sample selection with noise rate estimation in noise learning of medical image analysis

arXiv 2312.15233 ↗First posted 23 Dec 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

In the field of medical image analysis, deep learning models have demonstrated remarkable success in enhancing diagnostic accuracy and efficiency. However, the reliability of these models is heavily dependent on the quality of training data, and the existence of label noise (errors in dataset annotations) of medical image data presents a significant challenge. This paper introduces a new sample selection method that enhances the performance of neural networks when trained on noisy datasets. Our approach features estimating the noise rate of a dataset by analyzing the distribution of loss values using Linear Regression. Samples are then ranked according to their loss values, and potentially noisy samples are excluded from the dataset. Additionally, we employ sparse regularization to further enhance the noise robustness of our model. Our proposed method is evaluated on five benchmark datasets and a real-life noisy medical image dataset. Notably, two of these datasets contain 3D medical images. The results of our experiments show that our method outperforms existing noise-robust learning methods, particularly in scenarios with high noise rates. Key words: noise-robust learning, medical image analysis, noise rate estimation, sample selection, sparse regularization

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 46 references that are in the map. First posted on arXiv 23 Dec 2023.

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References · 32

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20 more
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