# Meta-learning for skin cancer detection using Deep Learning Techniques

arXiv: https://arxiv.org/abs/2104.10775 · HTML: https://mlsanity.com/paper/2104.10775/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Understanding data augmentation for classification: when to warp?](https://mlsanity.com/paper/1609.08764/) (2016)
- [A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification](https://mlsanity.com/paper/1910.04176/) (2019)
- [Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies](https://mlsanity.com/paper/1812.01716/) (2018)
- [Exploring the similarity of medical imaging classification problems](https://mlsanity.com/paper/1706.03509/) (2017)

## Built on by (most cited first, 1 of 1)

- [Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics](https://mlsanity.com/paper/2503.23819/) (2025)

## Abstract (arXiv)

This study focuses on automatic skin cancer detection using a Meta-learning approach for dermoscopic images. The aim of this study is to explore the benefits of the generalization of the knowledge extracted from non-medical data in the classification performance of medical data and the impact of the distribution shift problem within limited data by using a simple class and distribution balancer algorithm. In this study, a small sample of a combined dataset from 3 different sources was used to fine-tune a ResNet model pre-trained on non-medical data. The results show an increase in performance on detecting melanoma, malignant (skin cancer), and benign moles with the prior knowledge obtained from images of everyday objects from the ImageNet dataset by 20 points. These findings suggest that features from non-medical images can be used towards the classification of skin moles and that the distribution of the data affects the performance of the model.