IEEE J Biomed Health Inform. 2022 Sep;26(9):4563-4574. doi: 10.1109/JBHI.2022.3192010. Epub 2022 Sep 9.
Accurate histological subtype classification between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using computed tomography (CT) images is of great importance to assist clinicians in determining treatment and therapy plans for non-small cell lung cancer (NSCLC) patients. Although current deep learning approaches have achieved promising progress in this field, they are often difficult to capture efficient tumor representations due to inadequate training data, and in consequence show limited performance. In this study, we propose a novel and effective reconstruction-assisted feature encoding network (RAFENet) for histological subtype classification by leveraging an auxiliary image reconstruction task to enable extra guidance and regularization for enhanced tumor feature representations. Different from existing reconstruction-assisted methods that directly use generalizable features obtained from shared encoder for primary task, a dedicated task-aware encoding module is utilized in RAFENet to perform refinement of generalizable features. Specifically, a cascade of cross-level non-local blocks are introduced to progressively refine generalizable features at different levels with the aid of lower-level task-specific information, which can successfully learn multi-level task-specific features tailored to histological subtype classification. Moreover, in addition to widely adopted pixel-wise reconstruction loss, we introduce a powerful semantic consistency loss function to explicitly supervise the training of RAFENet, which combines both feature consistency loss and prediction consistency loss to ensure semantic invariance during image reconstruction. Extensive experimental results show that RAFENet effectively addresses the difficult issues that cannot be resolved by existing reconstruction-based methods and consistently outperforms other state-of-the-art methods on both public and in-house NSCLC datasets. Supplementary material is available at https://github.com/lhch1994/Rafenet_sup_material.
利用计算机断层扫描 (CT) 图像准确地对腺癌 (ADC) 和鳞状细胞癌 (SCC) 进行组织亚型分类，对于帮助临床医生为非小细胞肺癌 (NSCLC) 患者制定治疗和治疗计划非常重要。尽管目前的深度学习方法在这一领域已经取得了有希望的进展，但由于训练数据不足，它们往往难以捕捉到有效的肿瘤表示，因此表现出有限的性能。在本研究中，我们提出了一种新颖而有效的基于重建的特征编码网络 (RAFENet)，通过利用辅助图像重建任务为增强的肿瘤特征表示提供额外的指导和正则化，从而实现组织亚型分类。与现有的基于重建的方法不同，这些方法直接使用从共享编码器获得的可泛化特征用于主要任务，RAFENet 中使用了专门的任务感知编码模块来细化可泛化特征。具体来说，引入级联的跨层非局部块，在较低层任务特定信息的辅助下，逐步细化不同层次的可泛化特征，从而成功学习针对组织亚型分类的多级任务特定特征。此外，除了广泛采用的像素级重建损失外，我们还引入了一种强大的语义一致性损失函数来显式监督 RAFENet 的训练，该函数结合了特征一致性损失和预测一致性损失，以确保图像重建过程中的语义不变性。广泛的实验结果表明，RAFENet 有效地解决了现有基于重建的方法无法解决的难题，并在公共和内部 NSCLC 数据集上始终优于其他最先进的方法。补充材料可在 https://github.com/lhch1994/Rafenet_sup_material 上获取。
