SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations
SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations
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DOI：
10.1016/j.media.2023.102867
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发表时间：
2023-06-21
影响因子：
10.9
通讯作者：
Liu, Zaiyi
中科院分区：
文献类型：
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作者：
Pan, Xipeng;Cheng, Jijun;Liu, Zaiyi
全切片图像的高通量核分割与分类在生物分析、临床诊断和精准医学中具有重要意义。随着CNN算法的进步和数据集的不断增长，在核分割和分类方面取得了长足的进步。然而，很少有研究从数据分布不平衡和形态特征多样化两方面考虑如何合理处理核异质性。由于数据分布的不平衡，少数类可能被多数类所主导，而形态特征的多样化可能导致分割结果脆弱。本研究提出一种成本敏感的多任务学习（SMILE）框架来解决数据异构问题。基于核分割和分类中最流行的多任务学习主干，我们提出了一种多任务相关注意（MTCA）方法来对多个高相关任务进行特征交互，从而更好地学习特征表示。提出了一种代价敏感的学习策略，通过增加对少数类错误分类的惩罚来解决数据分布不平衡的问题。在此基础上，提出了一种基于粗变细标记控制分水岭方案的后处理步骤，以缓解核尺寸大、轮廓不清晰时的脆弱分割问题。大量实验表明，该方法在CoNSeP和MoNuSAC 2020数据集上达到了最先进的性能。代码可在https://github.com/ panxipeng/ nucle_segandcls获得。
High throughput nuclear segmentation and classification of whole slide images (WSIs) is crucial to biological analysis, clinical diagnosis and precision medicine. With the advances of CNN algorithms and the continuously growing datasets, considerable progress has been made in nuclear segmentation and classification. However, few works consider how to reasonably deal with nuclear heterogeneity in the following two aspects: imbalanced data distribution and diversified morphology characteristics. The minority classes might be dominated by the majority classes due to the imbalanced data distribution and the diversified morphology characteristics may lead to fragile segmentation results. In this study, a cost -Sensitive MultI-task LEarning (SMILE) framework is conducted to tackle the data heterogeneity problem. Based on the most popular multi-task learning backbone in nuclei segmentation and classification, we propose a multi-task correlation attention (MTCA) to perform feature interaction of multiple high relevant tasks to learn better feature representation. A cost-sensitive learning strategy is proposed to solve the imbalanced data distribution by increasing the penalization for the error classification of the minority classes. Furthermore, we propose a novel post-processing step based on the coarse-to-fine marker-controlled watershed scheme to alleviate fragile segmentation when nuclei are with large size and unclear contour. Extensive experiments show that the proposed method achieves state-of-the-art performances on CoNSeP and MoNuSAC 2020 datasets. The code is available at: https://github.com/ panxipeng/nuclear_segandcls.