medical-6

Skin Lesion Segmentation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in skin-lesion-segmentation-13

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Dataset

Best Model

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ISIC 2017

University of Waterloo skin cancer database

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Use these libraries to find skin-lesion-segmentation-13 models and implementations

Datasets

University of Waterloo skin cancer database

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Most implemented papers

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.

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RECOD Titans at ISIC Challenge 2017

S. Avila, Michel Fornaciali, Eduardo Valle, Afonso Menegola, Julia Tavares, Lin Tzy Li•Mon Mar 13 2017

This extended abstract describes the participation of RECOD Titans in parts 1 and 3 of the ISIC Challenge 2017 "Skin Lesion Analysis Towards Melanoma Detection" (ISBI 2017).

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Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

Sergio Escalera, Reza Azad, M. Fathy, Maryam Asadi-Aghbolaghi•Fri Aug 30 2019

This paper proposes an extension of U-Net, Bi-directional ConvLSTM U- net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which the full advantages of U -Net, bi- directional Conv lSTM (BConvL STM) and the mechanism of dense convolutions are taken.

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CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

S. Ourselin, T. Vercauteren, J. Deprest, Guotai Wang, M. Aertsen, Shaoting Zhang, Ran Gu, Rui Huang, Tao Song•Mon Sep 21 2020

This work makes extensive use of multiple attentions in a CNN architecture and proposes a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time.

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Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging

J. Branch, Diego Alberto Patiño Cortes, Jonathan Avendaño•Mon Aug 20 2018

The presented method is capable of dealing with segmentation problems commonly found in dermoscopic images such as hair removal, oil bubbles, changes in illumination, and reflections images without any additional steps.

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DSNet: Automatic Dermoscopic Skin Lesion Segmentation

Md. Kamrul Hasan, F. I. Tushar, Lavsen Dahal, P. Samarakoon, Robert Martí Marly•Mon Jul 08 2019

A new and automatic semantic segmentation network for robust skin lesion segmentation named Dermoscopic Skin Network (DSNet) is presented and is able to provide better-segmented masks on two different test datasets which can lead to better performance in melanoma detection.

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MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation

Hritam Basak, Rohit Kundu, R. Sarkar•Sat Mar 26 2022

An Artificial Intelligence (AI) framework for supervised skin lesion segmentation employing the deep learning approach, called MFSNet (Multi-Focus Segmentation Network), which outperforms state-of-the-art methods, justifying the reliability of the framework.

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Calibrate the Inter-Observer Segmentation Uncertainty via Diagnosis-First Principle

Mingkui Tan, Junde Wu, Huihui Fang, Yanwu Xu, Hoayi Xiong, Lixin Duan, Weihua Yang, Huiying Liu•Thu Aug 04 2022

Experimental results show that the proposed Diagnosis-First segmentation Framework (DiFF) can effectively calibrate the segmentation uncertainty, and thus significantly facilitate the corresponding disease diagnosis, which outperforms previous state-of-the-art multi-rater learning methods.

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Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers

Reza Azad, A. Kazerouni, Ehsan Khodapanah Aghdam, Abin Jose, D. Merhof, Alaa Sulaiman, Afshin Bozorgpour•Thu Aug 24 2023

This paper addresses the local feature deficiency of the Transformer model by carefully re-designing the self-attention map to produce accurate dense prediction in medical images and proposes a multi-scale context enhancement block within skip connections to adaptively model inter-scale dependencies to overcome the semantic gap among stages of the encoder and decoder modules.

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U-Net V2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation

Yao Peng, Milan Sonka, D. Z. Chen•Tue Nov 28 2023

U-Net v2 is introduced, a new robust and efficient U-Net variant for medical image segmentation that aims to augment the infusion of semantic information into low-level features while simultaneously refining high-level features with finer details.

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