DEEP LEARNING APPLICATIONS IN BONE MINERAL DENSITY ESTIMATION, SPINE VERTEBRA DETECTION, AND LIVER TUMOR SEGMENTATION

Abstract

As the aging population and related health concerns emerge in more countries than ever, we face many challenges such as the availability, quality, and cost of medical resources. Thanks to the development of machine learning and computer vision in recent years, Deep Learning (DL) can help solve some medical problems. The diagnosis of various diseases (such as spine disorders, low bone mineral density, and liver cancer) relies on X-rays or Computed Tomography (CT). DL models could automatically analyze these radiography scans and help with the diagnosis. Different organs and diseases have distinct characteristics, requiring customized algorithms and models. In this dissertation, we investigate several Computer Aided-Diagnosis (CAD) tasks and present corresponding DL solutions. Deep Learning has multiple advantages. Firstly, DL models could uncover underlying health issues invisible to humans. One example is the opportunistic screening of Osteoporosis through chest X-ray. We develop DL models, utilizing chest film to predict bone mineral density, which helps prevent bone fractures. Humans could not tell anything about bone density in the chest film, but DL models could reliably make the prediction. The second advantage is accuracy and efficiency. Reading radiography is tedious, requiring years of expertise. This is particularly true when a radiologist needs to localize potential liver tumors by looking through tens of CT slices, spending several minutes. Deep learning models could localize and identify the tumors within seconds, greatly reducing human labor. Experiments show DL models can pick up small tumors, which are hardly seen by the naked eye. Attention should be paid to deep learning limitations. Firstly, DL models lack explainability. Deep learning models store diagnostic knowledge and statistical patterns in their parameters, which are obscure to humans. Secondly, uncertainty exists for rare diseases. If not exposed to rare cases, the models would yield uncertain outcomes. Thirdly, training AI models are subject to high-quality data but the labeling quality varies in clinical practice. Despite the challenges and issues, deep learning models are promising to promote medical diagnosis in society.