Skin Cancer Segmentation

8 papers with code • 2 benchmarks • 3 datasets

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Libraries

Use these libraries to find Skin Cancer Segmentation models and implementations

Most implemented papers

U-Net: Convolutional Networks for Biomedical Image Segmentation

labmlai/annotated_deep_learning_paper_implementations 18 May 2015

There is large consent that successful training of deep networks requires many thousand annotated training samples.

Road Extraction by Deep Residual U-Net

rishikksh20/ResUnet 29 Nov 2017

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

LeeJunHyun/Image_Segmentation 20 Feb 2018

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

DebeshJha/2020-CBMS-DoubleU-Net 8 Jun 2020

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

MaczekO/AttentionNetworkProject 8 Feb 2019

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

CristianLazoQuispe/skin-lesion-segmentation-using-pix2pix 25 Apr 2019

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

hashbanger/Skin_Lesion_Segmentation International Conference On Artificial Intelligence And Speech Technology (AIST 2019) 2019

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

marinbenc/medical-polar-training IEEE Access 2021

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.