الفهرس 
A Machine Learning Approach for Analyzing Medical Images
ContentsOnly 14 pages are availabe for public view
 |  | from | 122 |  |  |
| | | from | 122 | | |
Abstract
Writing radiology reports in hospitals is a time-consuming activity that also necessitates
competence from the radiologists involved. This thesis offers a deep learning model for
generating radiological reports automatically based on a chest x-ray or a mammogram.
There are three steps to our work: (1) Fine-tune a Chexnet that has already been trained to
predict certain tags from an image. (2) Calculate weighted semantic features from the pretrained embeddings of the predicted tag. (3) Condition a pre-trained GPT2 model on the
visual and semantic features to construct the comprehensive medical reports. The model
was trained on the publicly available IU-XRay dataset that contains chest X-ray images
and their corresponding reports, and on the CDD-CESM dataset that contains mammograms and their reports. The CDD-CESM dataset is a new dataset that we collected from
Egypt’s National Institute of Cancer as no publicly-available dataset exists for mammograms with full-text report