# Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism

arXiv: https://arxiv.org/abs/2111.08708 · HTML: https://mlsanity.com/paper/2111.08708/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 52 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [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)](https://mlsanity.com/paper/1710.05006/) (2017)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [Medical Image Analysis using Convolutional Neural Networks: A Review](https://mlsanity.com/paper/1709.02250/) (2017)
- [A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI](https://mlsanity.com/paper/1512.07951/) (2015)
- [Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network](https://mlsanity.com/paper/1703.00577/) (2017)
- [SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks](https://mlsanity.com/paper/1805.10241/) (2018)
- [Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods](https://mlsanity.com/paper/1312.7345/) (2013)
- [Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks](https://mlsanity.com/paper/1703.04197/) (2017)
- [Extraction of Skin Lesions from Non-Dermoscopic Images Using Deep Learning](https://mlsanity.com/paper/1609.02374/) (2016)
- [Machine Learning and Deep Learning Methods for Building Intelligent Systems in Medicine and Drug Discovery: A Comprehensive Survey](https://mlsanity.com/paper/2107.14037/) (2021)
- [Class dependency based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of Tuberculosis from chest x-rays](https://mlsanity.com/paper/2108.04329/) (2021)

## Built on by (most cited first, 1 of 1)

- [A survey, review, and future trends of skin lesion segmentation and classification](https://mlsanity.com/paper/2208.12232/) (2022)

## Abstract (arXiv)

Segmenting skin lesions from dermoscopic images is essential for diagnosing skin cancer. But the automatic segmentation of these lesions is complicated due to the poor contrast between the background and the lesion, image artifacts, and unclear lesion boundaries. In this work, we present a deep learning model for the segmentation of skin lesions from dermoscopic images. To deal with the challenges of skin lesion characteristics, we designed a multi-scale feature extraction module for extracting the discriminative features. Further in this work, two attention mechanisms are developed to refine the post-upsampled features and the features extracted by the encoder. This model is evaluated using the ISIC2018 and ISBI2017 datasets. The proposed model outperformed all the existing works and the top-ranked models in two competitions.