# Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosis from X-Ray Images

arXiv: https://arxiv.org/abs/2110.10381 · HTML: https://mlsanity.com/paper/2110.10381/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Teacher-Student Curriculum Learning](https://mlsanity.com/paper/1707.00183/) (2017)
- [Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks](https://mlsanity.com/paper/1802.03796/) (2018)
- [Dynamic Curriculum Learning for Imbalanced Data Classification](https://mlsanity.com/paper/1901.06783/) (2019)
- [Domain-adversarial neural networks to address the appearance variability of histopathology images](https://mlsanity.com/paper/1707.06183/) (2017)
- [Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs](https://mlsanity.com/paper/1807.07532/) (2018)
- [Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification](https://mlsanity.com/paper/2009.13698/) (2020)
- [Curriculum semi-supervised segmentation](https://mlsanity.com/paper/1904.05236/) (2019)
- [Medical-based Deep Curriculum Learning for Improved Fracture Classification](https://mlsanity.com/paper/2004.00482/) (2020)
- [Multi-Task Curriculum Transfer Deep Learning of Clothing Attributes](https://mlsanity.com/paper/1610.03670/) (2016)
- [A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification](https://mlsanity.com/paper/1707.06978/) (2017)
- [Curriculum Learning for Multi-Task Classification of Visual Attributes](https://mlsanity.com/paper/1708.08728/) (2017)
- [Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning](https://mlsanity.com/paper/1703.01290/) (2017)
- [Curriculum Learning of Visual Attribute Clusters for Multi-Task Classification](https://mlsanity.com/paper/1709.06664/) (2017)
- [CASSL: Curriculum Accelerated Self-Supervised Learning](https://mlsanity.com/paper/1708.01354/) (2017)

## Built on by (most cited first, 3 of 3)

- [Deep Curriculum Learning in Task Space for Multi-Class Based Mammography Diagnosis](https://mlsanity.com/paper/2110.11320/) (2021)
- [Human not in the loop: objective sample difficulty measures for Curriculum Learning](https://mlsanity.com/paper/2302.01243/) (2023)
- [Patient-Level Elbow Abnormality Detection: Leakage-Aware Evaluation of Learned Preprocessing, Calibration, and Triage-Oriented Operating Points](https://mlsanity.com/paper/2606.31348/) (2026)

## Abstract (arXiv)

Elbow fractures are one of the most common fracture types. Diagnoses on elbow fractures often need the help of radiographic imaging to be read and analyzed by a specialized radiologist with years of training. Thanks to the recent advances of deep learning, a model that can classify and detect different types of bone fractures needs only hours of training and has shown promising results. However, most existing deep learning models are purely data-driven, lacking incorporation of known domain knowledge from human experts. In this work, we propose a novel deep learning method to diagnose elbow fracture from elbow X-ray images by integrating domain-specific medical knowledge into a curriculum learning framework. In our method, the training data are permutated by sampling without replacement at the beginning of each training epoch. The sampling probability of each training sample is guided by a scoring criterion constructed based on clinically known knowledge from human experts, where the scoring indicates the diagnosis difficultness of different elbow fracture subtypes. We also propose an algorithm that updates the sampling probabilities at each epoch, which is applicable to other sampling-based curriculum learning frameworks. We design an experiment with 1865 elbow X-ray images for a fracture/normal binary classification task and compare our proposed method to a baseline method and a previous method using multiple metrics. Our results show that the proposed method achieves the highest classification performance. Also, our proposed probability update algorithm boosts the performance of the previous method.