Abstract
Automatic identification of patients with luminal and non-luminal subtypes during a routine mammography screening can support clinicians in streamlining breast cancer therapy planning. Recent machine learning techniques have shown promising results in molecular subtype classification in mammography; however, they are highly dependent on pixel-level annotations, handcrafted, and radiomic features. In this work, we provide initial insights into the luminal subtype classification in full mammogram images trained using only image-level labels. Transfer learning is applied from a breast abnormality classification task, to finetune a ResNet-18-based luminal versus non-luminal subtype classification task. We present and compare our results on the publicly available CMMD dataset and show that our approach significantly outperforms the baseline classifier by achieving a mean AUC score of 0.6688 and a mean F1 score of 0.6693 on the test dataset. The improvement over baseline is statistically significant, with a p-value of p<0.0001.
Abstract (translated)
在常规乳腺癌筛查中,自动识别使用Luminal和非Luminal亚型的患者可以提供临床医生Streamlining乳腺癌治疗规划的支持。最近的机器学习技术在乳腺癌分子亚型分类方面取得了令人瞩目的结果;然而,它们的高度依赖像素级别的注释、手工分析和射频特征。在这个工作中,我们提供了对使用图像级别标签训练的全乳腺癌图像Luminal亚型分类的初步见解。迁移学习从乳腺癌异常分类任务中应用,以优化基于ResNet-18的Luminal和非Luminal亚型分类任务。我们在公开可用CMMD数据集上呈现并比较了我们的结果,并表明我们的方法在测试数据集上显著优于基准分类器,平均AUC得分为0.6688,平均F1得分为0.6693。与基准分类器相比,改进度是显著的,p值小于0.0001。
URL
https://arxiv.org/abs/2301.09282