Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning
This addresses the challenge of reducing radiologists' time and effort in annotating X-rays for anomaly detection, though it is incremental as it builds on existing self-supervised techniques.
The paper tackles the problem of anomaly detection in X-ray images by proposing SALAD, a self-supervised method that avoids the need for annotated anomalies, and it improves state-of-the-art methods by a wide margin on NIH Chest X-rays and MURA datasets.
Deep anomaly detection models using a supervised mode of learning usually work under a closed set assumption and suffer from overfitting to previously seen rare anomalies at training, which hinders their applicability in a real scenario. In addition, obtaining annotations for X-rays is very time consuming and requires extensive training of radiologists. Hence, training anomaly detection in a fully unsupervised or self-supervised fashion would be advantageous, allowing a significant reduction of time spent on the report by radiologists. In this paper, we present SALAD, an end-to-end deep self-supervised methodology for anomaly detection on X-Ray images. The proposed method is based on an optimization strategy in which a deep neural network is encouraged to represent prototypical local patterns of the normal data in the embedding space. During training, we record the prototypical patterns of normal training samples via a memory bank. Our anomaly score is then derived by measuring similarity to a weighted combination of normal prototypical patterns within a memory bank without using any anomalous patterns. We present extensive experiments on the challenging NIH Chest X-rays and MURA dataset, which indicate that our algorithm improves state-of-the-art methods by a wide margin.