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medical-8

Skin Cancer Segmentation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in skin-cancer-segmentation-16

Trend

Dataset

Best Model

Actions

Kaggle Skin Lesion Segmentation

PH2

Libraries

Use these libraries to find skin-cancer-segmentation-16 models and implementations

nikhilroxtomar/semantic-segmentatio…

2 papers 135

Datasets

PH2

SD-198

ISIC 2020 Challenge Dataset

Subtasks

No subtasks available.

Most implemented papers

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

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Paper Graph

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.

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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.

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DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

Debesh Jha, M. Riegler, P. Halvorsen, Dag Johansen, Haavard D. Johansen•Sun Jun 07 2020

Encouraging results 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.

763 0

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Focusnet: An Attention-Based Fully Convolutional Network for Medical Image Segmentation

S. Manandhar, Chaitanya Kaul, Nick E. Pears•Thu Feb 07 2019

A novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate Convolutional autoencoder is proposed, showing highly competitive performance when compared with U-Net and it’s residual variant.

145 0

Paper Graph

Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks

Md. Zahangir Alom, T. Taha, V. Asari, Theus H. Aspiras•Wed Apr 24 2019

This paper proposes a new DL architecture, the NABLA-N network, with better feature fusion techniques in decoding units for dermoscopic image segmentation tasks, and shows better quantitative and qualitative results with the same or fewer network parameters compared to other methods.

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Training on Polar Image Transformations Improves Biomedical Image Segmentation

Marin Benčević, I. Galić, Marija Habijan, D. Babin•Thu Dec 31 2020

This paper proposes a general way to improve neural network segmentation performance and data efficiency on medical imaging segmentation tasks where the goal is to segment a single roughly elliptically distributed object and proposes two different approaches to obtaining an optimal polar origin.

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Skin Cancer Segmentation | State-of-the-Art