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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