# Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning

arXiv: https://arxiv.org/abs/1809.02568 · HTML: https://mlsanity.com/paper/1809.02568/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Bulk Production Augmentation Towards Explainable Melanoma Diagnosis](https://mlsanity.com/paper/2103.02198/) (2021)
- [Semi-Supervised Relational Contrastive Learning](https://mlsanity.com/paper/2304.05047/) (2023)
- [Systematic Integration of Attention Modules into CNNs for Accurate and Generalizable Medical Image Diagnosis](https://mlsanity.com/paper/2509.05343/) (2025)

## Abstract (arXiv)

In this report, we introduce the outline of our system in Task 3: Disease Classification of ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection. We fine-tuned multiple pre-trained neural network models based on Squeeze-and-Excitation Networks (SENet) which achieved state-of-the-art results in the field of image recognition. In addition, we used the mean teachers as a semi-supervised learning framework and introduced some specially designed data augmentation strategies for skin lesion analysis. We confirmed our data augmentation strategy improved classification performance and demonstrated 87.2% in balanced accuracy on the official ISIC2018 validation dataset.