Wang Xunping, Yuan Wei
School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
Co-Creation Center for Disaster Resilience, International Research Institute of Disaster Science, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-8572, Japan.
iScience. 2024 Apr 26;27(6):109826. doi: 10.1016/j.isci.2024.109826. eCollection 2024 Jun 21.
New breast cancer cases have surpassed lung cancer, becoming the world's most prevalent cancer. Despite advancing medical image analysis, deep learning's lack of interpretability limits its adoption among pathologists. Hence, a nuclei-level prior knowledge constrained multiple instance learning (MIL) (NPKC-MIL) for breast whole slide image (WSI) classification is proposed. NPKC-MIL primarily involves the following steps: Initially, employing the transfer learning to extract patch-level features and aggregate them into slide-level features through attention pooling. Subsequently, abstract the extracted nuclei as nodes, establish nucleus topology using the K-NN (K-Nearest Neighbors, K-NN) algorithm, and create handcrafted features for nodes. Finally, combine patch-level deep learning features with nuclei-level handcrafted features to fine-tune classification results generated by slide-level deep learning features. The experimental results demonstrate that NPKC-MIL outperforms current comparable deep learning models. NPKC-MIL expands the analytical dimension of WSI classification tasks and integrates prior knowledge into deep learning models to improve interpretability.
新的乳腺癌病例已超过肺癌，成为全球最常见的癌症。尽管医学图像分析不断进步，但深度学习缺乏可解释性限制了其在病理学家中的应用。因此，提出了一种用于乳腺全切片图像（WSI）分类的细胞核水平先验知识约束多实例学习（MIL）（NPKC-MIL）方法。NPKC-MIL主要包括以下步骤：首先，利用迁移学习提取补丁级特征，并通过注意力池化将其聚合为切片级特征。随后，将提取的细胞核抽象为节点，使用K近邻（K-NN）算法建立细胞核拓扑结构，并为节点创建手工特征。最后，将补丁级深度学习特征与细胞核级手工特征相结合，对切片级深度学习特征生成的分类结果进行微调。实验结果表明，NPKC-MIL优于当前可比的深度学习模型。NPKC-MIL扩展了WSI分类任务的分析维度，并将先验知识集成到深度学习模型中以提高可解释性。
