medical-7

Lesion Segmentation

3260 papers • 126 benchmarks • 313 datasets

Lesion segmentation is the task of segmenting out lesions from other objects in medical based images. ( Image credit: D-UNet )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in lesion-segmentation-14

Trend

Dataset

Best Model

Actions

ISIC 2018

Anatomical Tracings of Lesions After Stroke (ATLAS)

Libraries

i

Use these libraries to find lesion-segmentation-14 models and implementations

PaddlePaddle/PaddleSeg

4 papers 8,248

Datasets

University of Waterloo skin cancer database

Subtasks

No subtasks available.

Most implemented papers

U-Net: Convolutional Networks for Biomedical Image Segmentation

O. Ronneberger, T. Brox, P. Fischer•Sun May 17 2015

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

90626

Content

University of Waterloo skin cancer database

BUS 2017 Dataset B

Anatomical Tracings of Lesions After Stroke (ATLAS)

ISIC 2017

ISLES-2015

ISIC 2018 Task 1

PH2

HAM10000

FelixGruen/tensorflow-u-net

4 papers 36

open-mmlab/mmsegmentation

3 papers 7,398

osmr/imgclsmob

3 papers 2,917

divamgupta/image-segmentation-keras

3 papers 2,831

y-ouali/pytorch_segmentation

3 papers 1,564

rezazad68/BCDU-Net

3 papers 674

Media-Smart/vedaseg

3 papers 408

qubvel/segmentation_models

2 papers 4,602

Kamnitsask/deepmedic

2 papers 995

PRBonn/bonnet

2 papers 322

JanMarcelKezmann/TensorFlow-Advance…

2 papers 149

nikhilroxtomar/semantic-segmentatio…

2 papers 137

petko-nikolov/pysemseg

2 papers 38

Jo-dsa/SemanticSeg

2 papers 26

samson6460/tf2_Segmentation

2 papers 16

PH2

0

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

Alex Kendall, Vijay Badrinarayanan, R. Cipolla•Sun Nov 01 2015

Quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures, including FCN and DeconvNet.

17098 0

Paper Graph

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Yukun Zhu, G. Papandreou•Tue Feb 06 2018

This work extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries and applies the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network.

15638 0

Paper Graph

Pyramid Scene Parsing Network

Xiaogang Wang, Jiaya Jia, Jianping Shi, Xiaojuan Qi, Hengshuang Zhao•Sat Dec 03 2016

This paper exploits the capability of global context information by different-region-based context aggregation through the pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet) to produce good quality results on the scene parsing task.

13635 0

Paper Graph

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

N. Codella, David Gutman, Brian Helba, A. Halpern, M. E. Celebi, M. Marchetti, V. Rotemberg, P. Tschandl, S. Dusza, Aadi Kalloo, K. Liopyris, H. Kittler•Fri Feb 08 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.

1558 0

Paper Graph

Road Extraction by Deep Residual U-Net

Zhengxin Zhang, Qingjie Liu, Yunhong Wang•Tue Nov 28 2017

A semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area extraction, which outperforms all the comparing methods and demonstrates its superiority over recently developed state of the arts methods.

2453 0

Paper Graph

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)

N. Codella, Brian Helba, A. Halpern, D. Gutman, M. E. Celebi, M. Marchetti, N. Mishra•Tue May 03 2016

The design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge are described, to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer.

2470 0

Paper Graph

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

Md. Zahangir Alom, T. Taha, C. Yakopcic, Mahmudul Hasan, V. Asari•Mon Feb 19 2018

A Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual convolutional neural Network (RRCNN), which are named RU-Net and R2U-Net respectively are proposed, which show superior performance on segmentation tasks compared to equivalent models including U-nets and residual U- net.

1190 0

Paper Graph

A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation

Nabila Abraham, N. Khan•Wed Oct 17 2018

A generalized focal loss function based on the Tversky index is proposed to address the issue of data imbalance in medical image segmentation and improves the attention U-Net model by incorporating an image pyramid to preserve contextual features.

849 0

Paper Graph

Boundary loss for highly unbalanced segmentation

Christian Desrosiers, Eric Granger, Ismail Ben Ayed, J. Dolz, H. Kervadec, Jihene Bouchtiba•Wed Dec 12 2018

Inspired by graph-based optimization techniques for computing active-contour flows, a non-symmetric L2 distance on the space of contours as a regional integral is expressed, which avoids completely local differential computations involving contour points.

613 0

Paper Graph

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