Key-Exchange Convolutional Auto-Encoder for Data Augmentation in Early Knee Osteoarthritis Detection

2023-02-26

AI Summary

To address the scarcity and high annotation cost of early knee osteoarthritis (KOA) X-ray data, alongside the limited generalizability of conventional augmentation methods, this paper proposes the Key-pathology Exchange Convolutional Autoencoder (KECAE). KECAE innovatively introduces anatomy-guided key-region localization and a pathology-feature exchange mechanism, enabling controllable and interpretable lesion-feature transfer in the latent space to generate high-fidelity synthetic images that preserve anatomical plausibility, diagnostic authenticity, and inter-sample diversity. A multi-objective hybrid loss function—integrating reconstruction loss, supervised classification loss, and feature disentanglement constraints—is designed to enhance clinical credibility. Evaluated on multiple downstream classifiers, KECAE achieves up to a 1.98% accuracy improvement. Blind assessment by radiologists confirms high expert consensus on both anatomical fidelity and diagnostic realism of the synthesized images.

Abstract

Knee Osteoarthritis (KOA) is a common musculoskeletal condition that significantly affects mobility and quality of life, particularly in elderly populations. However, training deep learning models for early KOA classification is often hampered by the limited availability of annotated medical datasets, owing to the high costs and labour-intensive nature of data labelling. Traditional data augmentation techniques, while useful, rely on simple transformations and fail to introduce sufficient diversity into the dataset. To address these challenges, we propose the Key-Exchange Convolutional Auto-Encoder (KECAE) as an innovative Artificial Intelligence (AI)-based data augmentation strategy for early KOA classification. Our model employs a convolutional autoencoder with a novel key-exchange mechanism that generates synthetic images by selectively exchanging key pathological features between X-ray images, which not only diversifies the dataset but also ensures the clinical validity of the augmented data. A hybrid loss function is introduced to supervise feature learning and reconstruction, integrating multiple components, including reconstruction, supervision, and feature separation losses. Experimental results demonstrate that the KECAE-generated data significantly improve the performance of KOA classification models, with accuracy gains of up to 1.98% across various standard and state-of-the-art architectures. Furthermore, a clinical validation study involving expert radiologists confirms the anatomical plausibility and diagnostic realism of the synthetic outputs. These findings highlight the potential of KECAE as a robust tool for augmenting medical datasets in early KOA detection.

Problem

• Knee Osteoarthritis Detection
• Image Annotation Limitations
• Traditional Image Augmentation Shortcomings

Innovation

• KECAE
• Convolutional Autoencoder
• Knee Osteoarthritis Imaging