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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
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.
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
Ranked #2 on Lung Nodule Segmentation on LUNA
In this paper a simple and computationally efficient approach as per the complexity has been presented for Automatic Skin Lesion Segmentation using a Deep Learning architecture called SegNet including some additional specifications for the improvisation of the results.
Ranked #1 on Skin Cancer Segmentation on PH2
Several DL architectures have been proposed for classification, segmentation, and detection tasks in medical imaging and computational pathology.
We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder.