Classifying Anti-nuclear Antibodies HEp-2 Images: A Benchmarking Platform

There has been an ongoing effort in improving reliability and consistency of pathology test results due to their critical role in making an accurate diagnosis. One way to do this is by applying image-based Computer Aided Diagnosis (CAD) systems. This paper proposes a comprehensive benchmarking platform comprising over 1,000 images to evaluate CAD systems for the Anti-Nuclear Antibody (ANA) test via the Indirect Immunofluorescence (IIF) protocol applied on Human Epithelial Type 2 (HEp-2) cells. While prior works in this domain have primarily focussed on classifying individual cell images derived from ANA IIF HEp-2 images, our proposed benchmarking platform goes beyond this by considering the ANA IIF HEp-2 image classification problem. Generally the existing works derive an ANA IIF HEp-2 image label from the dominant pattern of the cell images (we call this approach baseline). In this work, we argue that this approach cannot be used to achieve an acceptable performance, thus, the problem of classifying ANA IIF HEp-2 images (or ANA images in short) is still largely unexplored. To demonstrate that, we propose a simple-yet-effective CAD system which is inspired from the recent success of object bank representation in the object classification domain. We evaluate the proposed system, the baseline and a recent CAD system and show that our proposed system considerably outperforms the others.