# U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation

arXiv: https://arxiv.org/abs/2210.16399 · HTML: https://mlsanity.com/paper/2210.16399/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [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)
- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [Learning a Discriminative Feature Network for Semantic Segmentation](https://mlsanity.com/paper/1804.09337/) (2018)
- [CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation](https://mlsanity.com/paper/2009.10549/) (2020)
- [Data Augmentation for Skin Lesion Analysis](https://mlsanity.com/paper/1809.01442/) (2018)
- [FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation](https://mlsanity.com/paper/1902.03091/) (2019)
- [Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation](https://mlsanity.com/paper/2003.05056/) (2020)

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

- [AGILE: Approach-based Grasp Inference Learned from Element Decomposition](https://mlsanity.com/paper/2402.01303/) (2024)

## Abstract (arXiv)

According to WHO[1], since the 1970s, diagnosis of melanoma skin cancer has been more frequent. However, if detected early, the 5-year survival rate for melanoma can increase to 99 percent. In this regard, skin lesion segmentation can be pivotal in monitoring and treatment planning. In this work, ten models and four augmentation configurations are trained on the ISIC 2016 dataset. The performance and overfitting are compared utilizing five metrics. Our results show that the U-Net-Resnet50 and the R2U-Net have the highest metrics value, along with two data augmentation scenarios. We also investigate CBAM and AG blocks in the U-Net architecture, which enhances segmentation performance at a meager computational cost. In addition, we propose using pyramid, AG, and CBAM blocks in a sequence, which significantly surpasses the results of using the two individually. Finally, our experiments show that models that have exploited attention modules successfully overcome common skin lesion segmentation problems. Lastly, in the spirit of reproducible research, we implement models and codes publicly available.