  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.
