Galazis Christoforos, Wu Huiyi, Goryanin Igor
Department of Computing, Imperial College London, London SW7 2AZ, UK.
National Heart & Lung Institute, Imperial College London, London SW7 2AZ, UK.
Diagnostics (Basel). 2025 Feb 25;15(5):549. doi: 10.3390/diagnostics15050549.
Early and accurate detection of breast cancer is crucial for improving treatment outcomes and survival rates. To achieve this, innovative imaging technologies such as microwave radiometry (MWR)-which measures internal tissue temperature-combined with advanced diagnostic methods like deep learning are essential. To address this need, we propose a hierarchical self-contrastive model for analyzing MWR data, called Joint-MWR (J-MWR). J-MWR focuses on comparing temperature variations within an individual by analyzing corresponding sub-regions of the two breasts, rather than across different samples. This approach enables the detection of subtle thermal abnormalities that may indicate potential issues. We evaluated J-MWR on a dataset of 4932 patients, demonstrating improvements over existing MWR-based neural networks and conventional contrastive learning methods. The model achieved a Matthews correlation coefficient of 0.74 ± 0.02, reflecting its robust performance. These results emphasize the potential of intra-subject temperature comparison and the use of deep learning to replicate traditional feature extraction techniques, thereby improving accuracy while maintaining high generalizability.
早期准确检测乳腺癌对于改善治疗效果和提高生存率至关重要。为此，创新的成像技术，如测量内部组织温度的微波辐射测量法（MWR），与深度学习等先进诊断方法相结合至关重要。为满足这一需求，我们提出了一种用于分析MWR数据的分层自对比模型，称为联合MWR（J-MWR）。J-MWR专注于通过分析双侧乳房的相应子区域来比较个体内部的温度变化，而非跨不同样本进行比较。这种方法能够检测出可能表明潜在问题的细微热异常。我们在一个包含4932名患者的数据集上评估了J-MWR，结果表明它优于现有的基于MWR的神经网络和传统对比学习方法。该模型的马修斯相关系数达到了0.74±0.02，反映出其稳健的性能。这些结果强调了个体内温度比较以及利用深度学习复制传统特征提取技术的潜力，从而在保持高通用性的同时提高准确性。
