Exploring the Efficacy of Base Data Augmentation Methods in Deep
  Learning-Based Radiograph Classification of Knee Joint Osteoarthritis
Fabi Prezja Leevi Annala Sampsa Kiiskinen Timo Ojala
Diagnosing knee joint osteoarthritis (KOA), a major cause of disability
worldwide, is challenging due to subtle radiographic indicators and the varied
progression of the disease. Using deep learning for KOA diagnosis requires
broad, comprehensive datasets. However, obtaining these datasets poses
significant challenges due to patient privacy concerns and data collection
restrictions. Additive data augmentation, which enhances data variability,
emerges as a promising solution. Yet, it's unclear which augmentation
techniques are most effective for KOA. This study explored various data
augmentation methods, including adversarial augmentations, and their impact on
KOA classification model performance. While some techniques improved
performance, others commonly used underperformed. We identified potential
confounding regions within the images using adversarial augmentation. This was
evidenced by our models' ability to classify KL0 and KL4 grades accurately,
with the knee joint omitted. This observation suggested a model bias, which
might leverage unrelated features for classification currently present in
radiographs. Interestingly, removing the knee joint also led to an unexpected
improvement in KL1 classification accuracy. To better visualize these
paradoxical effects, we employed Grad-CAM, highlighting the associated regions.
Our study underscores the need for careful technique selection for improved
model performance and identifying and managing potential confounding regions in
radiographic KOA deep learning.