Papers › Deep Learning for Medical Imaging From Diagnosis Prediction to its Counterfactual Explanation
Deep Learning for Medical Imaging From Diagnosis Prediction to its Counterfactual Explanation
Sumedha Singla
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.
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