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Nicholas Passantino

Domain Adaptation in Machine Learning for Medical Imaging​

Nicholas Passantino ’21

Faculty Mentor(s):
Joshua Stough, Computer Science
Funding Source:
Presidential Fellowship

Machine learning models do well in learning to classify images, but can undesirably learn features unique to the specific dataset they were trained on, and not to the desired content of the images themselves. In our work, we use various machine learning techniques to develop models that learn domain-invariant features between two popular datasets in medical imaging. These models take in images of the heart created via echocardiogram and output a segmentation of the images into the different components of the human heart. The desired end-goal is to increase the model’s accuracy on the secondary dataset while minimizing the decrease in accuracy on the initial dataset.​

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