Multi-scale self-guided attention for medical image segmentation

Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First, the use of multi-scale approaches, i.e., encoder-decoder architectures, leads to a redundant use of information, where similar low-level features are extracted multiple times at multiple scales... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Brain Tumor Segmentation BRATS 2018 MS-Dual-Guided Dice Score 0.8037 # 2
MSD 0.9 # 1
VS 93.08 # 1
Medical Image Segmentation CHAOS MRI Dataset MS-Dual-Guided Dice Score 86.75 # 1
MSD 66 # 1
VS 93.85 # 1
Medical Image Segmentation HSVM MS-Dual-Guided Dice Score 83.2 # 1
MSD 1.19 # 1
VS 94.45 # 1

Methods used in the Paper


METHOD TYPE
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