Medical image segmentation is the task of segmenting objects of interest in a medical image - for example organs or lesions.
( Image credit: IVD-Net )
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
CELL SEGMENTATION ELECTRON MICROSCOPY IMAGE SEGMENTATION IMAGE AUGMENTATION 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.
SOTA for Pancreas Segmentation on CT-150
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
SOTA for Lung Nodule Segmentation on LUNA
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
#10 best model for Semantic Segmentation on Cityscapes val
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
SOTA for Lesion Segmentation on ISLES-2015
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.
We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms.