8 papers with code • 2 benchmarks • 0 datasets
Pancreas segmentation is the task of segmenting out the pancreas from medical imaging.
Convolutional neural network
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets.
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 novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
Ranked #1 on Pancreas Segmentation on CT-150
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.
The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration.
Ranked #1 on Pancreas Segmentation on TCIA Pancreas-CT Dataset
Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans.
There is large consent that successful training of deep networks requires many thousand annotated training samples.