Abstract
Deep learning techniques have achieved promising performance for computer-aided diagnosis, which is beneficial to alleviate the workload of clinicians. However, due to the scarcity of diseased samples, medical image datasets suffer from an inherent imbalance, and lead diagnostic algorithms biased to majority categories. This degrades the diagnostic performance, especially in recognizing rare categories. Existing works formulate this challenge as long-tails and adopt decoupling strategies to mitigate the effect of the biased classifier. But these works only use the imbalanced dataset to train the encoder and resample data to re-train the classifier by discarding the samples of head categories, thereby restricting the diagnostic performance. To address these problems, we propose a Multi-view Relation-aware Consistency and Virtual Features Compensation (MRC-VFC) framework for long-tailed medical image classification in two stages. In the first stage, we devise a Multi-view Relation-aware Consistency (MRC) for representation learning, which provides the training of encoders with unbiased guidance in addition to the imbalanced supervision. In the second stage, to produce an impartial classifier, we propose the Virtual Features Compensation (VFC) to recalibrate the classifier by generating massive balanced virtual features. Compared with the resampling, VFC compensates the minority classes to optimize an unbiased classifier with preserving complete knowledge of the majority ones. Extensive experiments on two long-tailed public benchmarks confirm that our MRC-VFC framework remarkably outperforms state-of-the-art algorithms.
L. Pan and Y. Zhang—Equal contribution.
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This work was supported in part by the InnoHK program.
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Pan, L., Zhang, Y., Yang, Q., Li, T., Chen, Z. (2023). Combat Long-Tails in Medical Classification with Relation-Aware Consistency and Virtual Features Compensation. 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_2
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