There is large consent that successful training of deep networks requires many thousand annotated training samples.
Ranked #1 on Semantic Segmentation on STARE
Proposed CNN based segmentation approaches demonstrate how 2D segmentation using prior slices can provide similar results to 3D segmentation while maintaining good continuity in the 3D dimension and improved speed.
3D Medical Imaging Segmentation 4D Spatio Temporal Semantic Segmentation +3
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
Ranked #1 on Video Polyp Segmentation on SUN-SEG-Easy
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