Lesion Segmentation
208 papers with code • 10 benchmarks • 13 datasets
Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.
( Image credit: D-UNet )
Benchmarks
These leaderboards are used to track progress in Lesion Segmentation
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Libraries
Use these libraries to find Lesion Segmentation models and implementationsDatasets
HAM10000
DigestPath
ISIC 2018 Task 1
ISIC 2017 Task 1
FGADR
University of Waterloo skin cancer database
ISIC 2018 Task 3
Most implemented papers
U-Net: Convolutional Networks for Biomedical Image Segmentation
labmlai/annotated_deep_learning_paper_implementations
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
tensorflow/models
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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.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Pyramid Scene Parsing Network
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
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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.
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.
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
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This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge.
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
LeeJunHyun/Image_Segmentation
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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
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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
We propose a boundary loss, which takes the form of a distance metric on the space of contours, not regions.
