# A survey, review, and future trends of skin lesion segmentation and classification

arXiv: https://arxiv.org/abs/2208.12232 · HTML: https://mlsanity.com/paper/2208.12232/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 546 references that are in the map.
- First posted on arXiv 25 Aug 2022.

## Builds on (most cited first, 30 of 48)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [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)
- [A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context](https://mlsanity.com/paper/2008.07360/) (2020)
- [Interpretations are useful: penalizing explanations to align neural networks with prior knowledge](https://mlsanity.com/paper/1909.13584/) (2019)
- [Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond](https://mlsanity.com/paper/2102.01998/) (2021)
- [Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data](https://mlsanity.com/paper/1910.03910/) (2019)
- [Boundary-aware Transformers for Skin Lesion Segmentation](https://mlsanity.com/paper/2110.03864/) (2021)
- [Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification](https://mlsanity.com/paper/1901.07759/) (2019)
- [A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/1903.03313/) (2019)
- [Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images](https://mlsanity.com/paper/1610.04662/) (2016)
- [Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification](https://mlsanity.com/paper/2102.01284/) (2021)
- [DSNet: Automatic Dermoscopic Skin Lesion Segmentation](https://mlsanity.com/paper/1907.04305/) (2019)
- [Deep neural network or dermatologist?](https://mlsanity.com/paper/1908.06612/) (2019)
- [Melanoma Detection using Adversarial Training and Deep Transfer Learning](https://mlsanity.com/paper/2004.06824/) (2020)
- [Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation](https://mlsanity.com/paper/2002.08694/) (2020)
- [Skin Lesion Classification Using Hybrid Deep Neural Networks](https://mlsanity.com/paper/1702.08434/) (2017)
- [The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification](https://mlsanity.com/paper/2008.12602/) (2020)
- [A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentation](https://mlsanity.com/paper/1810.07075/) (2018)
- [Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition](https://mlsanity.com/paper/2106.03455/) (2021)
- [SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images](https://mlsanity.com/paper/2203.11490/) (2022)
- [Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting](https://mlsanity.com/paper/1905.02793/) (2019)
- [MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation](https://mlsanity.com/paper/2203.14341/) (2022)
- [Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification](https://mlsanity.com/paper/1809.03851/) (2018)
- [Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features](https://mlsanity.com/paper/1810.09487/) (2018)
- [Skin lesion detection based on an ensemble of deep convolutional neural network](https://mlsanity.com/paper/1705.03360/) (2017)
- [Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging](https://mlsanity.com/paper/1808.06759/) (2018)
- [Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification](https://mlsanity.com/paper/2006.14715/) (2020)
- [Skin Lesion Segmentation: U-Nets versus Clustering](https://mlsanity.com/paper/1710.01248/) (2017)
- [Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet](https://mlsanity.com/paper/1907.03220/) (2019)

## Built on by (most cited first, 21 of 21)

- [ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification](https://mlsanity.com/paper/2307.04136/) (2023)
- [DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images](https://mlsanity.com/paper/2305.12621/) (2023)
- [MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation](https://mlsanity.com/paper/2604.20286/) (2026)
- [USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation](https://mlsanity.com/paper/2309.13289/) (2023)
- [A Novel Perspective for Multi-modal Multi-label Skin Lesion Classification](https://mlsanity.com/paper/2409.12390/) (2024)
- [Skin Lesion Phenotyping via Nested Multi-modal Contrastive Learning](https://mlsanity.com/paper/2505.23709/) (2025)
- [What Can We Learn from Inter-Annotator Variability in Skin Lesion Segmentation?](https://mlsanity.com/paper/2508.09381/) (2025)
- [Deep Neural Networks Fused with Textures for Image Classification](https://mlsanity.com/paper/2308.01813/) (2023)
- [Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification](https://mlsanity.com/paper/2311.15625/) (2023)
- [Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution](https://mlsanity.com/paper/2404.16814/) (2024)
- [An analysis of data variation and bias in image-based dermatological datasets for machine learning classification](https://mlsanity.com/paper/2501.08962/) (2025)
- [DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation](https://mlsanity.com/paper/2602.19848/) (2026)
- [Ugly Ducklings or Swans: A Tiered Quadruplet Network with Patient-Specific Mining for Improved Skin Lesion Classification](https://mlsanity.com/paper/2309.09689/) (2023)
- [A Region of Interest Focused Triple UNet Architecture for Skin Lesion Segmentation](https://mlsanity.com/paper/2311.12581/) (2023)
- [ResNet101 and DAE for Enhance Quality and Classification Accuracy in Skin Cancer Imaging](https://mlsanity.com/paper/2403.14248/) (2024)
- [When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion Segmentation](https://mlsanity.com/paper/2411.09363/) (2024)
- [Toward Accessible Dermatology: Skin Lesion Classification Using Deep Learning Models on Mobile-Acquired Images](https://mlsanity.com/paper/2509.04800/) (2025)
- [An End-to-End Deep Learning Framework for Arsenicosis Diagnosis Using Mobile-Captured Skin Images](https://mlsanity.com/paper/2509.08780/) (2025)
- [DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile](https://mlsanity.com/paper/2511.10367/) (2025)
- [Implementation of a Skin Lesion Detection System for Managing Children with Atopic Dermatitis Based on Ensemble Learning](https://mlsanity.com/paper/2511.23082/) (2025)
- [Bridging Research and Practice: A Systematic Evaluation of Generalist and Dermatology-Specific Models in Clinical Skin Lesion Classification](https://mlsanity.com/paper/2610.03193/) (2026)

## Abstract (arXiv)

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis.