# SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks

arXiv: https://arxiv.org/abs/1805.10241 · HTML: https://mlsanity.com/paper/1805.10241/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

## Built on by (most cited first, 11 of 11)

- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [P2T: Pyramid Pooling Transformer for Scene Understanding](https://mlsanity.com/paper/2106.12011/) (2021)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/2112.01767/) (2021)
- [Dermo-DOCTOR: A framework for concurrent skin lesion detection and recognition using a deep convolutional neural network with end-to-end dual encoders](https://mlsanity.com/paper/2102.01824/) (2021)
- [LSSF-Net: Lightweight Segmentation with Self-Awareness, Spatial Attention, and Focal Modulation](https://mlsanity.com/paper/2409.01572/) (2024)
- [Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism](https://mlsanity.com/paper/2111.08708/) (2021)
- [Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images](https://mlsanity.com/paper/2305.19063/) (2023)
- [Contrastive and Selective Hidden Embeddings for Medical Image Segmentation](https://mlsanity.com/paper/2201.08779/) (2022)
- [Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation](https://mlsanity.com/paper/2402.08892/) (2024)
- [Multi-Granularity Vision Fastformer with Fusion Mechanism for Skin Lesion Segmentation](https://mlsanity.com/paper/2504.03108/) (2025)

## Abstract (arXiv)

Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than 100 images of size 384x384 per second on a recent GPU.