Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning

• URL: http://arxiv.org/abs/2010.09856v1
• Date: Mon, 19 Oct 2020 20:49:34 GMT
• Title: Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning
• Authors: Behzad Bozorgtabar, Dwarikanath Mahapatra, Guillaume Vray, Jean-Philippe Thiran
• Abstract summary: SALAD is 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. Our anomaly score is then derived by measuring similarity to a weighted combination of normal prototypical patterns within a memory bank.
• Score: 16.854288765350283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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