用多层级自对比学习提升微波乳腺癌检测精度
Multi-Tiered Self-Contrastive Learning for Medical Microwave Radiometry (MWR) Breast Cancer Detection
- 设计局部、区域、全局三层次分析模型，融合自对比学习
- 在4932例数据上实现0.74的马修相关系数，优于现有方法
- 适合关注医疗影像智能诊断与便携式筛查的研究者
提升乳腺癌检测与监测技术是医疗领域的关键目标，推动了新型成像技术和诊断方法的发展。本研究提出一种针对微波辐射计（MWR）乳腺癌检测的多层级自对比学习模型。该模型包含三个不同层级的子模型：局部-MWR（L-MWR）、区域-MWR（R-MWR）和全局-MWR（G-MWR），用于分析乳房内不同子区域的对比特征。这些模型通过联合-MWR（J-MWR）网络进行整合，利用各层级的自对比结果提升诊断准确性。基于4,932名女性患者的数据库，实验表明，J-MWR模型达到0.74 ± 0.018的马修相关系数，显著优于现有的MWR神经网络及对比学习方法。结果表明，自对比学习可有效提升MWR乳腺癌检测的准确性和泛化能力，为未来便携式点对点筛查技术提供重要支持。源代码已公开于：https://github.com/cgalaz01/self_contrastive_mwr。
原文摘要 · Abstract (English)
Improving breast cancer detection and monitoring techniques is a critical objective in healthcare, driving the need for innovative imaging technologies and diagnostic approaches. This study introduces a novel multi-tiered self-contrastive model tailored for microwave radiometry (MWR) in breast cancer detection. Our approach incorporates three distinct models: Local-MWR (L-MWR), Regional-MWR (R-MWR), and Global-MWR (G-MWR), designed to analyze varying sub-regional comparisons within the breasts. These models are integrated through the Joint-MWR (J-MWR) network, which leverages self-contrastive results at each analytical level to improve diagnostic accuracy. Utilizing a dataset of 4,932 female patients, our research demonstrates the efficacy of our proposed models. Notably, the J-MWR model achieves a Matthew's correlation coefficient of 0.74 $\pm$ 0.018, surpassing existing MWR neural networks and contrastive methods. These findings highlight the potential of self-contrastive learning techniques in improving the diagnostic accuracy and generalizability for MWR-based breast cancer detection. This advancement holds considerable promise for future investigations into enabling point-of-care testing. The source code is available at: https://github.com/cgalaz01/self_contrastive_mwr.
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