# Meta ordinal weighting net for improving lung nodule classification

arXiv: https://arxiv.org/abs/2102.00456 · HTML: https://mlsanity.com/paper/2102.00456/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 31 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Task-Agnostic Meta-Learning for Few-shot Learning](https://mlsanity.com/paper/1805.07722/) (2018)
- [Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation](https://mlsanity.com/paper/1910.13616/) (2019)
- [Self-Supervised Generalisation with Meta Auxiliary Learning](https://mlsanity.com/paper/1901.08933/) (2019)
- [Unimodal probability distributions for deep ordinal classification](https://mlsanity.com/paper/1705.05278/) (2017)
- [Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping](https://mlsanity.com/paper/1810.12494/) (2018)
- [TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process](https://mlsanity.com/paper/1703.00645/) (2017)
- [Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules](https://mlsanity.com/paper/2012.03480/) (2020)

## Built on by (most cited first, 4 of 4)

- [Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction](https://mlsanity.com/paper/2104.11436/) (2021)
- [Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning](https://mlsanity.com/paper/2212.04097/) (2022)
- [Faithful learning with sure data for lung nodule diagnosis](https://mlsanity.com/paper/2202.12515/) (2022)
- [Privacy Preserving Ordinal-Meta Learning with VLMs for Fine-Grained Fruit Quality Prediction](https://mlsanity.com/paper/2511.01449/) (2025)

## Abstract (arXiv)

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.