Abstract
In medical image analysis, imbalanced noisy dataset classification poses a long-standing and critical problem since clinical large-scale datasets often attain noisy labels and imbalanced distributions through annotation and collection. Current approaches addressing noisy labels and long-tailed distributions separately may negatively impact real-world practices. Additionally, the factor of class hardness hindering label noise removal remains undiscovered, causing a critical necessity for an approach to enhance the classification performance of noisy imbalanced medical datasets with various class hardness. To address this paradox, we propose a robust classifier that trains on a multi-stage noise removal framework, which jointly rectifies the adverse effects of label noise, imbalanced distribution, and class hardness. The proposed noise removal framework consists of multiple phases. Multi-Environment Risk Minimization (MER) strategy captures data-to-label causal features for noise identification, and the Rescaling Class-aware Gaussian Mixture Modeling (RCGM) learns class-invariant detection mappings for noise removal. Extensive experiments on two imbalanced noisy clinical datasets demonstrate the capability and potential of our method for boosting the performance of medical image classification.
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Acknowlegdement
This work described in this paper was supported in part by the Shenzhen Portion of Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone under HZQB-KCZYB-20200089. The work was also partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Number: T45-401/22-N) and by a grant from the Hong Kong Innovation and Technology Fund (Project Number: GHP/080/20SZ). The work was also partially supported by a grant from the National Key R &D Program of China (2022YFE0200700), a grant from the National Natural Science Foundation of China (Project No. 62006219), and a grant from the Natural Science Foundation of Guangdong Province (2022A1515011579).
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Li, J. et al. (2023). Learning Robust Classifier for Imbalanced Medical Image Dataset with Noisy Labels by Minimizing Invariant Risk. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_30
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DOI: https://doi.org/10.1007/978-3-031-43987-2_30
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