Efficient Transfer Learning for Cardiac landmark Localization Using Rotational Entropy

Samira Masoudi, Kevin Blansit, Naeim Bahrami, Albert Hsiao

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Transfer learning is a common technique to address model generalization among different sources, which requires additional annotated data. Herein, we proposed a novel strategy to select new data to be annotated for transfer learning of a landmark localization model, minimizing the time and effort for annotation and thus model generalization. A CNN model was initially trained using 1.5T images to localize the apex and mitral valve on the long axis cardiac MR images. Model performance on 3T images was reported poor, necessitating transfer learning to 3T images. \textit{Rotational entropy}, was introduced not only as a surrogate of model performance but as a metric which could be used to prioritize the most informative cases for transfer learning.
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Friday 7th July
Poster Session 3.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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