3 research outputs found
Deep Learning for Automated Medical Image Analysis
Medical imaging is an essential tool in many areas of medical applications,
used for both diagnosis and treatment. However, reading medical images and
making diagnosis or treatment recommendations require specially trained medical
specialists. The current practice of reading medical images is labor-intensive,
time-consuming, costly, and error-prone. It would be more desirable to have a
computer-aided system that can automatically make diagnosis and treatment
recommendations. Recent advances in deep learning enable us to rethink the ways
of clinician diagnosis based on medical images. In this thesis, we will
introduce 1) mammograms for detecting breast cancers, the most frequently
diagnosed solid cancer for U.S. women, 2) lung CT images for detecting lung
cancers, the most frequently diagnosed malignant cancer, and 3) head and neck
CT images for automated delineation of organs at risk in radiotherapy. First,
we will show how to employ the adversarial concept to generate the hard
examples improving mammogram mass segmentation. Second, we will demonstrate how
to use the weakly labeled data for the mammogram breast cancer diagnosis by
efficiently design deep learning for multi-instance learning. Third, the thesis
will walk through DeepLung system which combines deep 3D ConvNets and GBM for
automated lung nodule detection and classification. Fourth, we will show how to
use weakly labeled data to improve existing lung nodule detection system by
integrating deep learning with a probabilistic graphic model. Lastly, we will
demonstrate the AnatomyNet which is thousands of times faster and more accurate
than previous methods on automated anatomy segmentation.Comment: PhD Thesi