# Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels

arXiv: https://arxiv.org/abs/2203.07725 · HTML: https://mlsanity.com/paper/2203.07725/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 63 references that are in the map.
- First posted on arXiv 15 Mar 2022.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [Task-Agnostic Meta-Learning for Few-shot Learning](https://mlsanity.com/paper/1805.07722/) (2018)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [Self-Supervised Generalisation with Meta Auxiliary Learning](https://mlsanity.com/paper/1901.08933/) (2019)
- [On Loss Functions for Deep Neural Networks in Classification](https://mlsanity.com/paper/1702.05659/) (2017)
- [3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network](https://mlsanity.com/paper/1802.05656/) (2018)
- [Deep Regression Forests for Age Estimation](https://mlsanity.com/paper/1712.07195/) (2017)
- [Unimodal probability distributions for deep ordinal classification](https://mlsanity.com/paper/1705.05278/) (2017)
- [Label Distribution Learning Forests](https://mlsanity.com/paper/1702.06086/) (2017)
- [Learning to detect chest radiographs containing lung nodules using visual attention networks](https://mlsanity.com/paper/1712.00996/) (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)
- [DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification](https://mlsanity.com/paper/1709.05538/) (2017)
- [Ordinal Distribution Regression for Gait-based Age Estimation](https://mlsanity.com/paper/1905.11005/) (2019)
- [Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules](https://mlsanity.com/paper/2012.03480/) (2020)
- [Improving Breast Cancer Detection using Symmetry Information with Deep Learning](https://mlsanity.com/paper/1808.08273/) (2018)

## Built on by (most cited first, 7 of 7)

- [CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Prediction](https://mlsanity.com/paper/2304.08013/) (2023)
- [Meta-learning in healthcare: A survey](https://mlsanity.com/paper/2308.02877/) (2023)
- [Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework](https://mlsanity.com/paper/2210.05436/) (2022)
- [URL: Combating Label Noise for Lung Nodule Malignancy Grading](https://mlsanity.com/paper/2308.08772/) (2023)
- [Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests](https://mlsanity.com/paper/2402.14080/) (2024)
- [Splitting criteria for ordinal decision trees: an experimental study](https://mlsanity.com/paper/2412.13697/) (2024)
- [CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimation](https://mlsanity.com/paper/2301.06122/) (2023)

## Abstract (arXiv)

The performance of medical image classification has been enhanced by deep convolutional neural networks (CNNs), which are typically trained with cross-entropy (CE) loss. However, when the label presents an intrinsic ordinal property in nature, e.g., the development from benign to malignant tumor, CE loss cannot take into account such ordinal information to allow for better generalization. To improve model generalization with ordinal information, we propose a novel meta ordinal regression forest (MORF) method for medical image classification with ordinal labels, which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework. The merits of the proposed MORF come from the following two components: a tree-wise weighting net (TWW-Net) and a grouped feature selection (GFS) module. First, the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding tree. Hence, all the trees possess varying weights, which is helpful for alleviating the tree-wise prediction variance. Second, the GFS module enables a dynamic forest rather than a fixed one that was previously used, allowing for random feature perturbation. During training, we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian matrix. Experimental results on two medical image classification datasets with ordinal labels, i.e., LIDC-IDRI and Breast Ultrasound Dataset, demonstrate the superior performances of our MORF method over existing state-of-the-art methods.