Weakly Supervised Classification and Localization of Thorax Diseases on X-Ray images




Jose, Alinstein

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Deep learning has added a vast improvement to the already rapidly developing field of computer vision. The ability to solve many computer vision problems like image classification, object detection, localization, and tracking has grown significantly in terms of performance and efficiency in recent years when the field is equipped with state-of-the-art deep learning techniques. In this project, we focus on classification and localization for medical imaging. Specifically, in the first part of the project, we develop a deep neural network that predicts disease in Chest X-ray images. Recent advancements in transfer earning suggest using the pre-trained model and fine-tuning since it is shown to produce state-of-the-art results. Therefore, in this project, we use both the learning of a model from scratch and transfer learning to classify chest X-ray images. In the second part of the project, we tackle the unavailability of dense annotation of region-level bounding boxes of diseases in X-ray images and propose a method to locate disease regions in X-ray images by constructing a weakly supervised localization method.



Weakly Supervised Classification, Weakly Supervised Localization, X-Ray images, Thorax Diseases, Class Activation Map, Deep Learning, Convolution Neural Network, GradCAM, GradCAM++