Pancreas segmentation is the task of segmenting out the pancreas from medical imaging.
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
SOTA for Pancreas Segmentation on CT-150
Missing contextual information led to unsatisfying convergence in iterations, and that the fine stage sometimes produced even lower segmentation accuracy than the coarse stage.
Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans.
We propose a new scheme that approximates both trainable weights and neural activations in deep networks by ternary values and tackles the open question of backpropagation when dealing with non-differentiable functions.