EMvision Research Notes, page-2203

This paper talks about using deep learning to solve problems in medical imaging, specifically in electromagnetic imaging. The current methods have issues due to limited training data, which mostly includes simplified scenarios. The paper introduces a new approach with a diverse database containing various shapes and electrical properties, representing both healthy and abnormal tissues. This database helps train a universal deep-learning model for accurately reconstructing the electrical profiles in complex medical imaging. The study uses a dataset of 25,000 unique objects with random shapes and electrical attributes, enhancing training and reducing overfitting risk. The results indicate that the trained model can generalize well, showing promising performance in imaging diverse objects without needing additional algorithms.

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