# Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks

arXiv: https://arxiv.org/abs/1703.04197 · HTML: https://mlsanity.com/paper/1703.04197/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2017.

## Built on by (most cited first, 8 of 8)

- [Semi-supervised learning for medical image classification using imbalanced training data](https://mlsanity.com/paper/2108.08956/) (2021)
- [MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/2112.01767/) (2021)
- [Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism](https://mlsanity.com/paper/2111.08708/) (2021)
- [Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data](https://mlsanity.com/paper/2201.10650/) (2022)
- [A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval](https://mlsanity.com/paper/2208.00627/) (2022)
- [GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis](https://mlsanity.com/paper/2301.04410/) (2023)
- [Progressive Class-Wise Attention (PCA) Approach for Diagnosing Skin Lesions](https://mlsanity.com/paper/2306.07300/) (2023)
- [MDSkin-Net: Multi-Task Skin Lesion Analysis Driven by Pattern Analysis Priors and Spatial Alignment Regularization](https://mlsanity.com/paper/2609.30855/) (2026)

## Abstract (arXiv)

Malignant melanoma has one of the most rapidly increasing incidences in the world and has a considerable mortality rate. Early diagnosis is particularly important since melanoma can be cured with prompt excision. Dermoscopy images play an important role in the non-invasive early detection of melanoma [1]. However, melanoma detection using human vision alone can be subjective, inaccurate and poorly reproducible even among experienced dermatologists. This is attributed to the challenges in interpreting images with diverse characteristics including lesions of varying sizes and shapes, lesions that may have fuzzy boundaries, different skin colors and the presence of hair [2]. Therefore, the automatic analysis of dermoscopy images is a valuable aid for clinical decision making and for image-based diagnosis to identify diseases such as melanoma [1-4]. Deep residual networks (ResNets) has achieved state-of-the-art results in image classification and detection related problems [5-8]. In this ISIC 2017 skin lesion analysis challenge [9], we propose to exploit the deep ResNets for robust visual features learning and representations.