  Deep neural networks (DNN) have achieved unprecedented performance in
computer-vision tasks almost ubiquitously in business, technology, and science.
While substantial efforts are made to engineer highly accurate architectures
and provide usable model explanations, most state-of-the-art approaches are
first designed for natural vision and then translated to the medical domain.
This dissertation seeks to address this gap by proposing novel architectures
that integrate the domain-specific constraints of medical imaging into the DNN
model and explanation design.
