Lesion Segmentation

120 papers with code • 7 benchmarks • 9 datasets

Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.

( Image credit: D-UNet )


Use these libraries to find Lesion Segmentation models and implementations

Most implemented papers

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

PaddlePaddle/PaddleSeg 2 Nov 2015

We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

tensorflow/models ECCV 2018

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.

Pyramid Scene Parsing Network

hszhao/PSPNet CVPR 2017

Scene parsing is challenging for unrestricted open vocabulary and diverse scenes.

Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

marinbenc/medical-polar-training 9 Feb 2019

This work summarizes the results of the largest skin image analysis challenge in the world, hosted by the International Skin Imaging Collaboration (ISIC), a global partnership that has organized the world's largest public repository of dermoscopic images of skin.

Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)

chenwydj/ultra_high_resolution_segmentation 13 Oct 2017

This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge.

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.

A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

nabsabraham/focal-tversky-unet 18 Oct 2018

We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation.

Boundary loss for highly unbalanced segmentation

LIVIAETS/surface-loss 17 Dec 2018

We propose a boundary loss, which takes the form of a distance metric on the space of contours, not regions.

Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks

IBBM/Cascaded-FCN 20 Feb 2017

In the first step, we train a FCN to segment the liver as ROI input for a second FCN.