Pancreas segmentation is the task of segmenting out the pancreas from medical imaging.
Convolutional neural network
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
Ranked #1 on Cell Segmentation on DIC-HeLa
CELL SEGMENTATION COLORECTAL GLAND SEGMENTATION: ELECTRON MICROSCOPY IMAGE SEGMENTATION IMAGE AUGMENTATION LESION SEGMENTATION LUNG NODULE SEGMENTATION MULTI-TISSUE NUCLEUS 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.
Ranked #1 on 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.
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.
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.
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs (i. e., liver, kidney, and spleen) seems to be a solved problem as the state-of-the-art (SOTA) methods have achieved comparable results with inter-observer variability on existing benchmark datasets.
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.