Second-order multi-instance learning model for whole slide image classification
Second-order multi-instance learning model for whole slide image classification
复制标题
DOI：
10.1088/1361-6560/ac0f30
复制
发表时间：
2021-06
影响因子：
3.5
通讯作者：
Qian Wang-;Y. Zou;Jianxin Zhang;B. Liu
中科院分区：
文献类型：
--
作者：
Qian Wang-;Y. Zou;Jianxin Zhang;B. Liu
全切片组织病理学图像（WSI）在诊断乳腺癌淋巴结转移中发挥着至关重要的作用，其通常缺乏肿瘤区域的精细注释并且分辨率较高（通常为105×105像素）。当只有幻灯片级标签可用时，多实例学习已逐渐成为 WSI 分类的主导弱监督学习框架。在本文中，我们开发了一种新颖的二阶多实例学习方法（SoMIL），该方法具有由注意力机制和循环神经网络（RNN）堆叠而成的自适应聚合器，用于组织病理学图像分类。具体来说，该方法应用二阶池化模块（矩阵幂归一化协方差）进行弱监督学习框架的实例级特征提取，试图探索组织病理学图像深层特征的二阶统计。此外，我们利用高效的通道注意机制来自适应地突出显示最具辨别力的实例特征，然后使用 RNN 来更新幻灯片分类的最终袋级表示。 2016 Camelyon grand Challenge 淋巴结转移数据集的实验结果表明，与其他最先进的多实例学习方法相比，我们提出的 SoMIL 框架有了显着改进。此外，在 130 个 WSI 的外部验证中，SoMIL 还实现了令人印象深刻的曲线下面积，可与完全监督的框架相媲美。
Whole slide histopathology images (WSIs) play a crucial role in diagnosing lymph node metastasis of breast cancer, which usually lack fine-grade annotations of tumor regions and have large resolutions (typically 105 × 105 pixels). Multi-instance learning has gradually become a dominant weakly supervised learning framework for WSI classification when only slide-level labels are available. In this paper, we develop a novel second-order multiple instances learning method (SoMIL) with an adaptive aggregator stacked by the attention mechanism and recurrent neural network (RNN) for histopathological image classification. To be specific, the proposed method applies a second-order pooling module (matrix power normalization covariance) for instance-level feature extraction of weakly supervised learning framework, attempting to explore second-order statistics of deep features for histopathological images. Additionally, we utilize an efficient channel attention mechanism to adaptively highlight the most discriminative instance features, followed by an RNN to update the final bag-level representation for the slide classification. Experimental results on the lymph node metastasis dataset of 2016 Camelyon grand challenge demonstrate the significant improvement of our proposed SoMIL framework compared with other state-of-the-art multi-instance learning methods. Moreover, in the external validation on 130 WSIs, SoMIL also achieves an impressive area under the curve performance that competitive to the fully-supervised framework.