  Deep learning-based methods have achieved promising performance in early
detection and classification of lung nodules, most of which discard unsure
nodules and simply deal with a binary classification -- malignant vs benign.
Recently, an unsure data model (UDM) was proposed to incorporate those unsure
nodules by formulating this problem as an ordinal regression, showing better
performance over traditional binary classification. To further explore the
ordinal relationship for lung nodule classification, this paper proposes a meta
ordinal regression forest (MORF), which improves upon the state-of-the-art
ordinal regression method, deep ordinal regression forest (DORF), in three
major ways. First, MORF can alleviate the biases of the predictions by making
full use of deep features while DORF needs to fix the composition of decision
trees before training. Second, MORF has a novel grouped feature selection (GFS)
module to re-sample the split nodes of decision trees. Last, combined with GFS,
MORF is equipped with a meta learning-based weighting scheme to map the
features selected by GFS to tree-wise weights while DORF assigns equal weights
for all trees. Experimental results on the LIDC-IDRI dataset demonstrate
superior performance over existing methods, including the state-of-the-art
DORF.
