A semi-supervised pipeline with a dual-branch classification network, K-Means, and region growing segments acute ischemic stroke lesions using 460 slice-level labels and 5 pixel-level labels, achieving Dice 0.642.
& Glocker, B.,2015
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Automatic acute ischemic stroke lesion segmentation using semi-supervised learning
A semi-supervised pipeline with a dual-branch classification network, K-Means, and region growing segments acute ischemic stroke lesions using 460 slice-level labels and 5 pixel-level labels, achieving Dice 0.642.