Multi-Tiered Self-Contrastive Learning for Medical Microwave Radiometry (MWR) Breast Cancer Detection

• URL: http://arxiv.org/abs/2410.04636v1
• Date: Sun, 6 Oct 2024 21:51:02 GMT
• Title: Multi-Tiered Self-Contrastive Learning for Medical Microwave Radiometry (MWR) Breast Cancer Detection
• Authors: Christoforos Galazis, Huiyi Wu, Igor Goryanin,
• Abstract summary: This study introduces a novel multi-tiered self-contrastive model tailored for the application of microwave radiometry (MWR) breast cancer detection. Our approach encompasses three distinct models: Local-MWR (L-MWR), Regional-MWR (R-MWR), and Global-MWR (G-MWR) These models are cohesively integrated through the Joint-MWR (J-MWR) network, which leverages the self-contrastive data generated at each analytical level to enhance detection capabilities.
• Score: 0.25569800973362833
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The pursuit of enhanced breast cancer detection and monitoring techniques is a paramount healthcare objective, driving the need for innovative imaging technologies and diagnostic approaches. This study introduces a novel multi-tiered self-contrastive model tailored for the application of microwave radiometry (MWR) breast cancer detection. Our approach encompasses three distinct models: Local-MWR (L-MWR), Regional-MWR (R-MWR), and Global-MWR (G-MWR), each engineered to analyze varying sub-regional comparisons within the breasts. These models are cohesively integrated through the Joint-MWR (J-MWR) network, which leverages the self-contrastive data generated at each analytical level to enhance detection capabilities. Employing a dataset comprising 4,932 cases of female patients, our research showcases the effectiveness of our proposed models. Notably, the J-MWR model distinguishes itself by achieving a Matthews correlation coefficient of 0.74 $\pm$ 0.018, surpassing existing MWR neural networks and contrastive methods. These results highlight the significant potential of self-contrastive learning techniques in improving both the diagnostic accuracy and generalizability of MWR-based breast cancer detection processes. Such advancements hold considerable promise for further investigative and clinical endeavors. The source code is available at: https://github.com/cgalaz01/self_contrastive_mwr

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