In recent years, the use of diagnosing images has been increased dramatically. An entry-level task of diagnosing and reading Chest X-ray for radiologist but they ought to require a good knowledge and careful observation of anatomical principles, pathology and physiology for this complex reasonings. In many modern hospital’s, the tremendous number of x-ray images are stored in PACS (Picture Archiving and Communication System). The conditions of plethora been diagnosed by the sustainable number of chest X-Ray. Our aim is to predict the thorax disease categories through deep learning using chest x-rays and their first-pass specialist accuracy. In a paper, the main application that presents a pathology localization framework and multi-label unified weakly supervised image classification that can perceive the occurrence of afterward generation of the bounding box around the consistent and multiple pathologies. Due to considering of large image capacity, we adapt Deep Convolutional Neural Network (DCNN) architecture for weakly-supervised object localization, different pooling strategies, various multi-label CNN losses and measured against a baseline of softmax regression.
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation