论文标题
在皮肤病变分类上获胜门票的特性
Properties Of Winning Tickets On Skin Lesion Classification
论文作者
论文摘要
皮肤癌每年都会影响大量人群 - 自动化的皮肤癌检测算法可以极大地帮助临床医生。涉及深度学习模型的先前努力具有很高的检测准确性。但是，大多数模型都有大量的参数，甚至有一些作品，甚至使用了一组模型来实现良好的精度。在本文中，我们研究了一种最近提出的修剪技术，称为彩票票证假设。我们发现，与未经修复的网络相比，该网络的迭代修剪导致精度提高了精度，这意味着 - 可以将彩票假设应用于皮肤癌检测的问题，并且该假设可能导致较小的网络进行推理。我们还研究了通过性别和年龄创建的子组之间的准确性 - 发现某些子组的准确性比其他子组更大。
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.
