# Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture Classification

arXiv: https://arxiv.org/abs/2110.10383 · HTML: https://mlsanity.com/paper/2110.10383/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks](https://mlsanity.com/paper/1703.07047/) (2017)
- [Medical-based Deep Curriculum Learning for Improved Fracture Classification](https://mlsanity.com/paper/2004.00482/) (2020)
- [Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning](https://mlsanity.com/paper/1909.06326/) (2019)

## Abstract (arXiv)

Elbow fracture diagnosis often requires patients to take both frontal and lateral views of elbow X-ray radiographs. In this paper, we propose a multiview deep learning method for an elbow fracture subtype classification task. Our strategy leverages transfer learning by first training two single-view models, one for frontal view and the other for lateral view, and then transferring the weights to the corresponding layers in the proposed multiview network architecture. Meanwhile, quantitative medical knowledge was integrated into the training process through a curriculum learning framework, which enables the model to first learn from "easier" samples and then transition to "harder" samples to reach better performance. In addition, our multiview network can work both in a dual-view setting and with a single view as input. We evaluate our method through extensive experiments on a classification task of elbow fracture with a dataset of 1,964 images. Results show that our method outperforms two related methods on bone fracture study in multiple settings, and our technique is able to boost the performance of the compared methods. The code is available at https://github.com/ljaiverson/multiview-curriculum.