Medical knowledge-guided deep curriculum learning for elbow fracture diagnosis from x-ray images

Jun Luo; Gene Kitamura; Emine Doganay; Dooman Arefan; Shandong Wu

doi:10.1117/12.2582184

15 February 2021 Medical knowledge-guided deep curriculum learning for elbow fracture diagnosis from x-ray images

Jun Luo, Gene Kitamura, Emine Doganay, Dooman Arefan, Shandong Wu

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 1159712 (2021) https://doi.org/10.1117/12.2582184
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Jun Luo, Gene Kitamura, Emine Doganay, Dooman Arefan, and Shandong Wu "Medical knowledge-guided deep curriculum learning for elbow fracture diagnosis from x-ray images", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 1159712 (15 February 2021); https://doi.org/10.1117/12.2582184

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available