# Properties Of Winning Tickets On Skin Lesion Classification

arXiv: https://arxiv.org/abs/2008.12141 · HTML: https://mlsanity.com/paper/2008.12141/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing](https://mlsanity.com/paper/2103.06179/) (2021)
- [Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images](https://mlsanity.com/paper/2206.07422/) (2022)

## Abstract (arXiv)

Skin cancer affects a large population every year -- automated skin cancer detection algorithms can thus greatly help clinicians. Prior efforts involving deep learning models have high detection accuracy. However, most of the models have a large number of parameters, with some works even using an ensemble of models to achieve good accuracy. In this paper, we investigate a recently proposed pruning technique called Lottery Ticket Hypothesis. We find that iterative pruning of the network resulted in improved accuracy, compared to that of the unpruned network, implying that -- the lottery ticket hypothesis can be applied to the problem of skin cancer detection and this hypothesis can result in a smaller network for inference. We also examine the accuracy across sub-groups -- created by gender and age -- and it was found that some sub-groups show a larger increase in accuracy than others.