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.
CELL SEGMENTATION DATA AUGMENTATION ELECTRON MICROSCOPY IMAGE SEGMENTATION LESION SEGMENTATION LUNG NODULE SEGMENTATION PANCREAS SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
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 then apply our quantization algorithm to three datasets: (1) the Spinal Cord Gray Matter Segmentation (GM), (2) the ISBI challenge for segmentation of neuronal structures in Electron Microscopic (EM), and (3) the public National Institute of Health (NIH) dataset for pancreas segmentation in 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.