Meta ordinal weighting net for improving lung nodule classification

TL;DR

This paper introduces MOW-Net, a novel meta-learning approach for lung nodule classification that explicitly models ordinal relationships, improving accuracy especially for uncertain cases.

Contribution

The paper proposes MOW-Net, a meta ordinal weighting network that aligns training samples with a meta ordinal set and uses a meta cross-entropy loss for better ordinal regression.

Findings

MOW-Net outperforms existing ordinal regression methods in accuracy.

Significant improvement in classifying unsure lung nodules.

Meta learning scheme enhances model robustness.

Abstract

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.