# Medical-based Deep Curriculum Learning for Improved Fracture Classification

arXiv: https://arxiv.org/abs/2004.00482 · HTML: https://mlsanity.com/paper/2004.00482/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 19 of 19)

- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Balanced-MixUp for Highly Imbalanced Medical Image Classification](https://mlsanity.com/paper/2109.09850/) (2021)
- [Curriculum Direct Preference Optimization for Diffusion and Consistency Models](https://mlsanity.com/paper/2405.13637/) (2024)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Memory-aware curriculum federated learning for breast cancer classification](https://mlsanity.com/paper/2107.02504/) (2021)
- [From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervision](https://mlsanity.com/paper/2412.11154/) (2024)
- [Learning Rate Curriculum](https://mlsanity.com/paper/2205.09180/) (2022)
- [Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks](https://mlsanity.com/paper/2407.07853/) (2024)
- [Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosis from X-Ray Images](https://mlsanity.com/paper/2110.10381/) (2021)
- [CBM: Curriculum by Masking](https://mlsanity.com/paper/2407.05193/) (2024)
- [Advancing Brain Imaging Analysis Step-by-step via Progressive Self-paced Learning](https://mlsanity.com/paper/2407.16128/) (2024)
- [Deep Curriculum Learning in Task Space for Multi-Class Based Mammography Diagnosis](https://mlsanity.com/paper/2110.11320/) (2021)
- [Task Phasing: Automated Curriculum Learning from Demonstrations](https://mlsanity.com/paper/2210.10999/) (2022)
- [CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification](https://mlsanity.com/paper/2505.01741/) (2025)
- [From Uncertainty to Clarity: Uncertainty-Guided Class-Incremental Learning for Limited Biomedical Samples via Semantic Expansion](https://mlsanity.com/paper/2409.07757/) (2024)
- [Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation](https://mlsanity.com/paper/2510.23241/) (2025)
- [Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture Classification](https://mlsanity.com/paper/2110.10383/) (2021)
- [Human not in the loop: objective sample difficulty measures for Curriculum Learning](https://mlsanity.com/paper/2302.01243/) (2023)
- [Curriculum Based Multi-Task Learning for Parkinson's Disease Detection](https://mlsanity.com/paper/2302.13631/) (2023)

## Abstract (arXiv)

Current deep-learning based methods do not easily integrate to clinical protocols, neither take full advantage of medical knowledge. In this work, we propose and compare several strategies relying on curriculum learning, to support the classification of proximal femur fracture from X-ray images, a challenging problem as reflected by existing intra- and inter-expert disagreement. Our strategies are derived from knowledge such as medical decision trees and inconsistencies in the annotations of multiple experts, which allows us to assign a degree of difficulty to each training sample. We demonstrate that if we start learning "easy" examples and move towards "hard", the model can reach a better performance, even with fewer data. The evaluation is performed on the classification of a clinical dataset of about 1000 X-ray images. Our results show that, compared to class-uniform and random strategies, the proposed medical knowledge-based curriculum, performs up to 15% better in terms of accuracy, achieving the performance of experienced trauma surgeons.