Authors: Christoforos Galazis, Huiyi Wu, Igor Goryanin

Published on: October 06, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2410.04636

Summary

• What is new: A new multi-tiered self-contrastive model for microwave radiometry in breast cancer detection.
• Why this is important: Current imaging technologies and diagnostic approaches for breast cancer detection need improvement.
• What the research proposes: The introduction of L-MWR, R-MWR, and G-MWR models, integrated through the Joint-MWR network.
• Results: The Joint-MWR model achieved a Matthews correlation coefficient of 0.74 ± 0.018, outperforming existing methods.

Technical Details

Technological frameworks used: Self-contrastive learning

Models used: L-MWR, R-MWR, G-MWR, Joint-MWR network

Data used: 4,932 cases of female patients

Potential Impact

Medical imaging technology markets and companies focusing on breast cancer diagnostics

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