ADVERSARIAL EVOLVING NEURAL NETWORK FOR LONGITUDINAL KNEE OSTEOARTHRITIS PREDICTION
Abstract : Knee osteoarthritis (KOA) as a disabling joint disease has doubled in prevalence since the mid-20th century. Early diagnosis for the longitudinal KOA grades has been increasingly important for effective monitoring and intervention. Although recent studies have achieved promising performance for baseline KOA grading, longitudinal KOA grading has been seldom studied and the KOA domain knowledge has not been well explored yet.In this paper, a novel deep learning architecture, namely adversarial evolving neural network (A-ENN), is proposed for longitudinal grading of KOA severity As the disease progresses from mild to severe level, ENN involves the progression patterns for accurately characterizing the disease by comparing an input image it to the template images of different KL grades using convolution and deconvolution computations. In addition, an adversarial training scheme with a discriminato developed to obtain the evolution traces. Thus, the evolution traces as fine-grained domain knowledge are further fused with the general convolutional image representations for longitudinal grading. Note that ENN can be applied to other learning tasks together with existing deep architectures, in which the responses characterize progressive representations
? Note that ENN can be applied to other learning tasks together with existing deep architectures, in which the responses characterize progressive representations.
? Existing studies mainly address the automatic KOA grading task of a given radiology image by treating it as an image classification task with multiple classes (i.e., KL grades).
? Early studies follow a conventional machine learning pipeline in which the image pre-processing and feature extraction steps are required for a classification model
? As many deep learning architectures have been proposed for general image classification problems, such as convolutiaon neural networks (CNNs) (e.g., VGG and ResNet ) and transformer based networks (e.g., Visual Transformer (ViT) , some of these architectures have been also adopted for KOA grading
? Besides, there are also a few attempts to involve the domain knowledge of KOA into the development of deep learning based methods, such as the inclusion of the demographic features and the loss functions using the continuous grading property
? In this paper, a novel deep learning architecture, namely adversarial evolving neural network (A-ENN), is proposed for longitudinal grading of KOA severity.
? As the disease progresses from mild to severe level, ENN involves the progression patterns for accurately characterizing the disease by comparing an input image it to the template images of different KL grades using convolution and deconvolution computations.
? Therefore, in this paper, a novel deep learning architecture, namely adversarial evolving neural network (A-ENN), with an adversarial training scheme is proposed for fine-grained longitudinal KOA grading using X-Ray images collected from clinical assessments
? For this purpose, the Kellgren and Lawrence (KL) grading system has been widely used to quantify the severity of the disease in clinical practice [2], in which radiology imaging techniques are utilised for KL grading
? The proposed A-ENN method is evaluated on a widely used benchmark dataset - the Osteoarthritis Initiative (OAI) Dataset - for KOA grading and achieves an accuracy 62.6%.
? This formulation is consistent with the design of the widely used generative neural networks which usually involve an adversarial training scheme
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