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AttenScribble: Attentive Similarity Learning for Scribble-Supervised Medical Image Segmentation
This attentive similarity loss optimizes the alignment of FCN encoders, attention mapping and model prediction.
A dynamic interactive learning framework for automated 3D medical image segmentation
Many deep learning based automated medical image segmentation systems, in reality, face difficulties in deployment due to the cost of massive data annotation and high latency in model iteration.
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