Machine Learning based sampling of X-Ray images for a computer-aided detection of Tuberculosis

Abstract

Computer Aided Detection software relies on an annotated data set of X-rays to be developed. The annotation task requires extensive know-how and it is very time-consuming. This work presents a sampling method to select the most relevant images which will be annotated for the development of Tuberculosis screening platform based on machine learning algorithms. The sampling task optimizes the annotation process by reducing the number of images to be analyzed without compromising the diversity and the significance power of the images in the dataset. In this context, the image relevance is based on similarity and dissimilarity measurements. The experiment consisted in a deep learning feature engineering step, followed by topological analysis based on Self-Organizing Map and K-Means.