Skin Cancer Segmentation
8 papers with code • 2 benchmarks • 3 datasets
Libraries
Use these libraries to find Skin Cancer Segmentation models and implementationsMost implemented papers
U-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires many thousand annotated training samples.
Road Extraction by Deep Residual U-Net
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image 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.
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.
FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation
We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder.
Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks
Several DL architectures have been proposed for classification, segmentation, and detection tasks in medical imaging and computational pathology.
Skin Lesion Segmentation using SegNet with Binary Cross-Entropy
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.
Training on Polar Image Transformations Improves Biomedical Image Segmentation
We show that our method produces state-of-the-art results for lesion, liver, and polyp segmentation and performs better than most common neural network architectures for biomedical image segmentation.