Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging. On the example of a binary image classification task (breast cancer recognition), we show that pretraining a generative model for meaningful image augmentation helps enhance the performance of the resulting classifier. By augmenting the data, performance on downstream classification tasks could be improved even with a relatively small training set. We show that this "adversarial augmentation" yields promising results compared to classical image augmentation on the example of breast cancer classification.
翻译:受监督的深层次学习所依据的假设是,有足够的培训数据,这给数据应用于医学成像等几个领域带来了问题。关于二进制图像分类任务(乳腺癌识别)的例子,我们表明,为有意义的图像增强而预先培训一个基因化模型有助于提高由此产生的分类员的性能。通过增加数据,下游分类任务的绩效可以提高,即使培训数量相对较少。我们显示,与乳腺癌分类典型图像增强相比,这种“对抗性增强”与典型的乳腺癌分类相比,产生有希望的结果。