# DSNet: Automatic Dermoscopic Skin Lesion Segmentation

arXiv: https://arxiv.org/abs/1907.04305 · HTML: https://mlsanity.com/paper/1907.04305/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 22 of 22)

- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [A survey, review, and future trends of skin lesion segmentation and classification](https://mlsanity.com/paper/2208.12232/) (2022)
- [MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation](https://mlsanity.com/paper/2203.14341/) (2022)
- [AC-MAMBASEG: An adaptive convolution and Mamba-based architecture for enhanced skin lesion segmentation](https://mlsanity.com/paper/2405.03011/) (2024)
- [MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation](https://mlsanity.com/paper/2409.03062/) (2024)
- [Multi-class probabilistic atlas-based whole heart segmentation method in cardiac CT and MRI](https://mlsanity.com/paper/2102.01822/) (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)
- [TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation](https://mlsanity.com/paper/2408.09687/) (2024)
- [Enhancing Skin Disease Diagnosis: Interpretable Visual Concept Discovery with SAM](https://mlsanity.com/paper/2409.09520/) (2024)
- [COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing](https://mlsanity.com/paper/2102.06169/) (2021)
- [Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks](https://mlsanity.com/paper/2105.03995/) (2021)
- [LCAUnet: A skin lesion segmentation network with enhanced edge and body fusion](https://mlsanity.com/paper/2305.00837/) (2023)
- [Multi-task Explainable Skin Lesion Classification](https://mlsanity.com/paper/2310.07209/) (2023)
- [PL-Net: Progressive Learning Network for Medical Image Segmentation](https://mlsanity.com/paper/2110.14484/) (2021)
- [Weakly Supervised Human Skin Segmentation using Guidance Attention Mechanisms](https://mlsanity.com/paper/2302.04625/) (2023)
- [Channel Attention Separable Convolution Network for Skin Lesion Segmentation](https://mlsanity.com/paper/2309.01072/) (2023)
- [IARS SegNet: Interpretable Attention Residual Skip connection SegNet for melanoma segmentation](https://mlsanity.com/paper/2310.20292/) (2023)
- [Boosting Medical Image Segmentation Performance with Adaptive Convolution Layer](https://mlsanity.com/paper/2404.11361/) (2024)
- [LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation](https://mlsanity.com/paper/2405.15779/) (2024)
- [PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentation](https://mlsanity.com/paper/2606.18707/) (2026)
- [Prompt-Conditioned Channel Attention for Hierarchical Feature Modulation toward Anatomy-Agnostic Segmentation](https://mlsanity.com/paper/2608.20229/) (2026)

## Abstract (arXiv)

Automatic segmentation of skin lesion is considered a crucial step in Computer Aided Diagnosis (CAD) for melanoma diagnosis. Despite its significance, skin lesion segmentation remains a challenging task due to their diverse color, texture, and indistinguishable boundaries and forms an open problem. Through this study, we present a new and automatic semantic segmentation network for robust skin lesion segmentation named Dermoscopic Skin Network (DSNet). In order to reduce the number of parameters to make the network lightweight, we used depth-wise separable convolution in lieu of standard convolution to project the learned discriminating features onto the pixel space at different stages of the encoder. Additionally, we implemented U-Net and Fully Convolutional Network (FCN8s) to compare against the proposed DSNet. We evaluate our proposed model on two publicly available datasets, namely ISIC-2017 and PH2. The obtained mean Intersection over Union (mIoU) is 77.5 % and 87.0 % respectively for ISIC-2017 and PH2 datasets which outperformed the ISIC-2017 challenge winner by 1.0 % with respect to mIoU. Our proposed network also outperformed U-Net and FCN8s respectively by 3.6 % and 6.8 % with respect to mIoU on the ISIC-2017 dataset. Our network for skin lesion segmentation outperforms other methods and can provide better segmented masks on two different test datasets which can lead to better performance in melanoma detection. Our trained model along with the source code and predicted masks are made publicly available.