Biologically Inspired Anomaly Detection in Pap-Smear Images

Uterine Cervical Cancer is one of the most common forms of cancer in women worldwide. Papanicolau smear test is a well-known screening method of detecting abnormalities in the uterine cervix cells. In this paper we address the problem of anomaly detection in pap smear images. Our method avoids modeling the normal pap smear images which is a very complex task due to the large within class variance of the normal target appearance patterns. The problem is posed as a Visual Attention Mechanism. Indeed the human vision system actively seeks interesting regions in images to reduce the search export in tasks, such as anomaly detection. In this paper, we develop a new method for identifying salient regions in pap smear images and compare this to two previously reported approaches. We then consider how such machine-saliency methods can be used to improve human performance in a realistic anomaly detection task.

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