87 papers with code • 6 benchmarks • 7 datasets
Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.
( Image credit: D-UNet )
The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.
Ranked #2 on Semantic Segmentation on PASCAL VOC 2012 test (using extra training data)
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)
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
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.
With all three datasets combined, the current system compared to previous methods also attained a reliably higher cross-validation accuracy.
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
Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
These blocks adaptively recalibrate the channel-wise feature responses by utilizing a self-gating mechanism of the global information embedding of the feature maps.
Ranked #2 on Lesion Segmentation on ISIC 2018