Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays
This work addresses the challenge of reducing reliance on costly annotations for anomaly detection in medical imaging, though it appears incremental as it builds on existing self-supervised and semi-supervised techniques.
The paper tackles the problem of anomaly detection in musculoskeletal radiographs by proposing a self-taught semi-supervised method that leverages unlabeled data through pretext tasks and cross-sample similarity, outperforming baselines on the MURA dataset.
Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow. Supervised deep networks take for granted a large number of annotations by radiologists, which is often prohibitively very time-consuming to acquire. Moreover, supervised systems are tailored to closed set scenarios, e.g., trained models suffer from overfitting to previously seen rare anomalies at training. Instead, our approach's rationale is to use task agnostic pretext tasks to leverage unlabeled data based on a cross-sample similarity measure. Besides, we formulate a complex distribution of data from normal class within our framework to avoid a potential bias on the side of anomalies. Through extensive experiments, we show that our method outperforms baselines across unsupervised and self-supervised anomaly detection settings on a real-world medical dataset, the MURA dataset. We also provide rich ablation studies to analyze each training stage's effect and loss terms on the final performance.