173 papers with code • 30 benchmarks • 28 datasets
Medical image classification is the task of classifying objects of interest in a medical image.
( Image credit: IVD-Net )
There is large consent that successful training of deep networks requires many thousand annotated training samples.
Ranked #1 on Medical Image Segmentation on ISBI 2012 EM Segmentation (Warping Error metric)
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
Ranked #1 on Medical Image Segmentation on 2018 Data Science Bowl
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Ranked #3 on Scene Segmentation on SUN-RGBD
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.
UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation.
Ranked #1 on Medical Image Segmentation on LiTS2017
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
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.
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).
Ranked #1 on Medical Image Segmentation on EM (IoU metric)
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
Ranked #1 on Lesion Segmentation on ISLES-2015