Breast Cancer Detection via Multi-Tiered Self-Contrastive Learning in Microwave Radiometric Imaging

TL;DR

This paper introduces a new deep learning model for breast cancer detection using microwave radiometry, which improves accuracy by comparing temperature differences within the same person's breasts.

Contribution

The novel hierarchical self-contrastive model (J-MWR) focuses on intra-subject temperature comparisons for improved breast cancer detection.

Findings

J-MWR outperformed existing MWR-based neural networks and contrastive learning methods on a dataset of 4932 patients.

The model achieved a Matthews correlation coefficient of 0.74 ± 0.02, indicating strong performance and generalizability.

Abstract

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

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