Role of Class-specific Features in Various Classification Frameworks for Human Epithelial (HEp-2) Cell Images
This work addresses the problem of time-consuming manual diagnosis for autoimmune diseases, but it is incremental as it builds on existing classification frameworks with new feature definitions.
The paper tackled automating antinuclear antibody detection in human epithelial cell images to aid physicians by using class-specific features based on visual characteristics, achieving encouraging results compared to state-of-the-art methods.
The antinuclear antibody detection with human epithelial cells is a popular approach for autoimmune diseases diagnosis. The manual evaluation demands time, effort and capital, and automation in screening can greatly aid the physicians in these respects. In this work, we employ simple, efficient and visually more interpretable, class-specific features which defined based on the visual characteristics of each class. We believe that defining features with a good visual interpretation, is indeed important in a scenario, where such an approach is used in an interactive CAD system for pathologists. Considering that problem consists of few classes, and our rather simplistic feature definitions, frameworks can be structured as hierarchies of various binary classifiers. These variants include frameworks which are earlier explored and some which are not explored for this task. We perform various experiments which include traditional texture features and demonstrate the effectiveness of class-specific features in various frameworks. We make insightful comparisons between different types of classification frameworks given their silent aspects and pros and cons over each other. We also demonstrate an experiment with only intermediates samples for testing. The proposed work yields encouraging results with respect to the state-of-the-art and highlights the role of class-specific features in different classification frameworks.