Meta Ordinal Weighting Net For Improving Lung Nodule Classification

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-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 corresponding class-specific weight. We further propose a meta cross-entropy loss to optimize the network in a meta-learning scheme. Experimental results demonstrate that the MOW-Net achieves better accuracy than the state-of-theart ordinal regression methods, especially for the unsure class.

A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models

“…A multi-scale cost-sensitive neural network was proposed to mitigate the issue of insufficient labeled data and class imbalance ( 70 ). A soft activation mapping-based method meta-learning scheme was reported for interpretable lung nodule classification ( 71 ) and a meta ordinal set was further generated by the same research group by developing meta ordinal weighting network to explore the ordinal relationship between the data for lung nodule classification ( 72 ). Recently, DL models based on transformers ( 73 , 74 ) or combined with CNN and transformers ( 75 , 76 ) have been successfully applied for lung nodule detection and classification.…”

Strided Self-Supervised Low-Dose CT Denoising for Lung Nodule Classification

“…The deep learning-driven LDCT-based lung nodule classification is also a widespread topic in the communities of computer science and biomedical engineering. Most of the CNN-based LDCT lung nodule classification methods focus more on addressing the problems such as limited annotations of data (Lei et al 2021(Lei et al ,2020aShen et al 2015), and utilizing multiple information existed in the volumes (Hussein et al 2017;Lei et al 2020a, b;Setio et al 2016).…”