The progression of lung cancer implies the intrinsic ordinal relationship of lung nodules at different stages-from benign to unsure then to malignant. This problem can be solved by ordinal regression methods, which is between classification and regression due to its ordinal label. However, existing convolutional neural network (CNN)-based ordinal regression methods only focus on modifying classification head based on a randomly sampled mini-batch of data, ignoring the ordinal relationship resided in the data itself. In this paper, we propose a Meta Ordinal Weighting Network (MOW-Net) to explicitly align each training sample with a meta ordinal set (MOS) containing a few samples from all classes. During the training process, the MOW-Net learns a mapping from samples in MOS to the corresponding class-specific weight. In addition, we further propose a meta cross-entropy (MCE) loss to optimize the network in a meta-learning scheme. The experimental results demonstrate that the MOW-Net achieves better accuracy than the state-of-the-art ordinal regression methods, especially for the unsure class.

翻译：肺癌的演进意味着肺结核在不同阶段(从良阶段到不确定阶段,然后到恶性阶段)的内在正统关系。这个问题可以通过正反转法来解决,因为正反转法是分类和回归之间的,但是,基于正反转法的现有累进神经网络(CNN)的正反转法只是侧重于根据随机抽样的微型数据批量修改分类头,忽视数据本身的正反转关系。在本文中,我们提议建立一个Meta Ordinal Weight 网络(MOW-Net),将每个培训样本与包含所有班级少数样本的元反转法(MOS)明确协调起来。在培训过程中,MOW-Net从MOS的样本到相应的特定班级重量中学习了一次绘图。此外,我们进一步提议在元学习计划中采用元交叉吸附(MCE)损失来优化网络。实验结果表明,MOW-Net的精确度高于状态或非正反转法,特别是对于无法肯定的班级而言。