Abstract
An Imbalance-Effective Active Learning (IEAL) based deep neural network algorithm is proposed for the automatic detection of nucleus, lymphocyte and plasma cells in hepatitis diagnosis. The active sampling approach reduces the training sample annotation cost and mitigates extreme imbalances among the nucleus, lymphocytes and plasma samples. A Bayesian U-net model is developed by incorporating IEAL with basic U-Net. The testing results obtained using an in-house dataset consisting of 43 whole slide images (300 256 * 256 images) show that the proposed method achieves an equal or better performance compared than a basic U-net classifier using less than half the number of annotated samples.
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Li, CT. et al. (2020). Imbalance-Effective Active Learning in Nucleus, Lymphocyte and Plasma Cell Detection. In: Cardoso, J., et al. Interpretable and Annotation-Efficient Learning for Medical Image Computing. IMIMIC MIL3ID LABELS 2020 2020 2020. Lecture Notes in Computer Science(), vol 12446. Springer, Cham. https://doi.org/10.1007/978-3-030-61166-8_24
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