Abstract
Breast cancer, the second leading cause of cancer-related deaths globally, accounts for a quarter of all cancer cases [1]. To lower this death rate, it is crucial to detect tumors early, as early-stage detection significantly improves treatment outcomes. Advances in non-invasive imaging techniques have made early detection possible through computer-aided detection (CAD) systems which rely on traditional image analysis to identify malignancies. However, there is a growing shift towards deep learning methods due to their superior effectiveness. Despite their potential, deep learning methods often struggle with accuracy due to the limited availability of large-labeled datasets for training. To address this issue, our study introduces a Contrastive Learning (CL) framework, which excels with smaller labeled datasets. In this regard, we train Resnet-50 in semi supervised CL approach using similarity index on a large amount of unlabeled mammogram data. In this regard, we use various augmentation and transformations which help improve the performance of our approach. Finally, we tune our model on a small set of labelled data that outperforms the existing state of the art. Specifically, we observed a 96.7% accuracy in detecting breast cancer on benchmark datasets INbreast and MIAS.
Abstract (translated)
乳腺癌是全球癌症死亡的第二大原因，占所有癌症病例的四分之一[1]。为了降低这一死亡率，早期发现肿瘤至关重要，因为早期诊断可以显著改善治疗效果。非侵入性成像技术的进步使得通过计算机辅助检测（CAD）系统实现早期发现成为可能，这些系统依赖于传统的图像分析来识别恶性病变。然而，由于深度学习方法的优越性能，人们越来越倾向于采用这种方法。尽管深度学习方法具有潜力，但由于训练所需的大规模标注数据集有限，它们在准确性方面经常面临挑战。 为了解决这一问题，我们的研究引入了一种对比学习（CL）框架，在这种框架下，即使使用较小的标注数据集也能表现出色。具体来说，我们在半监督的对比学习环境中训练Resnet-50模型，并利用大量的未标记乳腺X光影像数据进行相似性索引计算。在这个过程中，我们采用了各种增强和变换技术来提高我们的方法性能。最终，我们将经过调优后的模型应用于少量标注的数据集上，并取得了超越现有技术水平的效果。 具体而言，在INbreast和MIAS等基准数据集中，我们实现了96.7%的乳腺癌检测准确率。
URL
https://arxiv.org/abs/2509.20474
