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In this project, we propose a deep learning based weakly supervised learning algorithm for cardiac adipose tissue segmentation using image-level labels. Based on ReLayNet, our proposed method can automatically segment the adipose tissue from normal myocardium tissue in pixel level. Compared with fully supervised learning methods, our model achieves competitive segmentation results on both accuracy and Dice coefficient within a database of OCT images of human cardiac tissue. Combined with the OCT image, the predicted adipose map could provide additional information for the guidance of cardiac radio frequency ablation.
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