用对比学习提升小样本乳腺癌检测准确率
A Contrastive Learning Framework for Breast Cancer Detection
- 基于对比学习框架，利用大量无标签乳腺钼靶图像预训练
- 在INbreast和MIAS数据集上达96.7%检测准确率
- 适合标注数据少但需高精度的医疗影像场景
乳腺癌是全球癌症相关死亡的第二大原因，占所有癌症病例的四分之一。为降低死亡率，早期检测至关重要，因其显著改善治疗效果。非侵入性成像技术的发展使计算机辅助检测（CAD）系统可通过传统图像分析识别恶性肿瘤。然而，深度学习方法因缺乏大规模标注数据而面临准确性挑战。为此，本研究提出一种对比学习（CL）框架，在小规模标注数据下表现优异。我们采用半监督对比学习方式，利用大量未标注乳腺钼靶图像对ResNet-50进行预训练，并结合多种数据增强与变换策略提升性能。最终在少量标注数据上微调模型，其在基准数据集INbreast和MIAS上的检测准确率达96.7%，超越现有最先进方法。
原文摘要 · Abstract (English)
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.
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