# ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection

arXiv: https://arxiv.org/abs/1703.00523 · HTML: https://mlsanity.com/paper/1703.00523/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2017.

## Built on by (most cited first, 30 of 34)

- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation](https://mlsanity.com/paper/2211.01784/) (2022)
- [Vision Mamba: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2405.04404/) (2024)
- [EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation](https://mlsanity.com/paper/2307.08473/) (2023)
- [U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2311.17791/) (2023)
- [Parametric Scattering Networks](https://mlsanity.com/paper/2107.09539/) (2021)
- [BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net models](https://mlsanity.com/paper/2112.03916/) (2021)
- [SAM-SP: Self-Prompting Makes SAM Great Again](https://mlsanity.com/paper/2408.12364/) (2024)
- [HMT-UNet: A hybird Mamba-Transformer Vision UNet for Medical Image Segmentation](https://mlsanity.com/paper/2408.11289/) (2024)
- [A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information](https://mlsanity.com/paper/2104.14353/) (2021)
- [MUCM-Net: A Mamba Powered UCM-Net for Skin Lesion Segmentation](https://mlsanity.com/paper/2405.15925/) (2024)
- [ConfounderGAN: Protecting Image Data Privacy with Causal Confounder](https://mlsanity.com/paper/2212.01767/) (2022)
- [Increasing biases can be more efficient than increasing weights](https://mlsanity.com/paper/2301.00924/) (2023)
- [Merging Context Clustering with Visual State Space Models for Medical Image Segmentation](https://mlsanity.com/paper/2501.01618/) (2025)
- [Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework](https://mlsanity.com/paper/2506.02854/) (2025)
- [Learning Multi-axis Representation in Frequency Domain for Medical Image Segmentation](https://mlsanity.com/paper/2312.17030/) (2023)
- [UCM-Net: A Lightweight and Efficient Solution for Skin Lesion Segmentation using MLP and CNN](https://mlsanity.com/paper/2310.09457/) (2023)
- [MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentation](https://mlsanity.com/paper/2401.11738/) (2024)
- [MHS-VM: Multi-Head Scanning in Parallel Subspaces for Vision Mamba](https://mlsanity.com/paper/2406.05992/) (2024)
- [Benchmarking of Lightweight Deep Learning Architectures for Skin Cancer Classification using ISIC 2017 Dataset](https://mlsanity.com/paper/2110.12270/) (2021)
- [Self-supervised learning for skin cancer diagnosis with limited training data](https://mlsanity.com/paper/2401.00692/) (2024)
- [Samba+: General and Accurate Salient Object Detection via A More Unified Mamba-based Framework](https://mlsanity.com/paper/2602.01593/) (2026)
- [PL-Net: Progressive Learning Network for Medical Image Segmentation](https://mlsanity.com/paper/2110.14484/) (2021)
- [Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data](https://mlsanity.com/paper/2201.10650/) (2022)
- [Leveraging Contextual Data Augmentation for Generalizable Melanoma Detection](https://mlsanity.com/paper/2212.05116/) (2022)
- [GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis](https://mlsanity.com/paper/2301.04410/) (2023)
- [Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review](https://mlsanity.com/paper/2309.00265/) (2023)
- [KM-UNet KAN Mamba UNet for medical image segmentation](https://mlsanity.com/paper/2501.02559/) (2025)
- [BiSeg-SAM: Weakly-Supervised Post-Processing Framework for Boosting Binary Segmentation in Segment Anything Models](https://mlsanity.com/paper/2504.01452/) (2025)

## Abstract (arXiv)

Our system addresses Part 1, Lesion Segmentation and Part 3, Lesion Classification of the ISIC 2017 challenge. Both algorithms make use of deep convolutional networks to achieve the challenge objective.