TY - JOUR
T1 - Curriculum Learning for Improved Femur Fracture Classification: Scheduling Data with Prior Knowledge and Uncertainty
AU - Jiménez Sánchez, Amelia
AU - Mateus, Diana
AU - Kirchhoff, Sonja
AU - Kirchhoff, Chlodwig
AU - Biberthaler, Peter
AU - Navab, Nassir
AU - González Ballester, Miguel Angel
AU - Piella, Gemma
PY - 2022/1
Y1 - 2022/1
N2 - An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients’ clinical outcome. We rely on the commonly used AO system, which describes a hierarchical knowledge tree classifying the images into types and subtypes according to the fracture’s location and complexity. In this paper, we propose a method for the automatic classification of proximal femur fractures into 3 and 7 AO classes based on a Convolutional Neural Network (CNN). As it is known, CNNs need large and representative datasets with reliable labels, which are hard to collect for the application at hand. In this paper, we design a curriculum learning (CL) approach that improves over the basic CNNs performance under such conditions. Our novel formulation reunites three curriculum strategies: individually weighting training samples, reordering the training set, and sampling subsets of data. The core of these strategies is a scoring function ranking the training samples. We define two novel scoring functions: one from domain-specific prior knowledge and an original self-paced uncertainty score. We perform experiments on a clinical dataset of proximal femur radiographs. The curriculum improves proximal femur fracture classification up to the performance of experienced trauma surgeons. The best curriculum method reorders the training set based on prior knowledge resulting into a classification improvement of 15%. Using the publicly available MNIST dataset, we further discuss and demonstrate the benefits of our unified CL formulation for three controlled and challenging digit recognition scenarios: with limited amounts of data, under class-imbalance, and in the presence of label noise. The code of our work is available at: https://github.com/ameliajimenez/curriculum-learning-prior-uncertainty.
AB - An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients’ clinical outcome. We rely on the commonly used AO system, which describes a hierarchical knowledge tree classifying the images into types and subtypes according to the fracture’s location and complexity. In this paper, we propose a method for the automatic classification of proximal femur fractures into 3 and 7 AO classes based on a Convolutional Neural Network (CNN). As it is known, CNNs need large and representative datasets with reliable labels, which are hard to collect for the application at hand. In this paper, we design a curriculum learning (CL) approach that improves over the basic CNNs performance under such conditions. Our novel formulation reunites three curriculum strategies: individually weighting training samples, reordering the training set, and sampling subsets of data. The core of these strategies is a scoring function ranking the training samples. We define two novel scoring functions: one from domain-specific prior knowledge and an original self-paced uncertainty score. We perform experiments on a clinical dataset of proximal femur radiographs. The curriculum improves proximal femur fracture classification up to the performance of experienced trauma surgeons. The best curriculum method reorders the training set based on prior knowledge resulting into a classification improvement of 15%. Using the publicly available MNIST dataset, we further discuss and demonstrate the benefits of our unified CL formulation for three controlled and challenging digit recognition scenarios: with limited amounts of data, under class-imbalance, and in the presence of label noise. The code of our work is available at: https://github.com/ameliajimenez/curriculum-learning-prior-uncertainty.
KW - Curriculum learning
KW - Noisy labels
KW - Class-imbalance
KW - Limited data
KW - Multi-class classification
KW - X-ray
KW - Bone fracture
KW - Data scheduler
KW - Self-paced learning
UR - https://arxiv.org/abs/2007.16102
U2 - https://doi.org/10.1016/j.media.2021.102273
DO - https://doi.org/10.1016/j.media.2021.102273
M3 - Journal article
SN - 1361-8415
VL - 75
JO - Medical Image Analysis
JF - Medical Image Analysis
IS - 102273
ER -