Medical Microwave Radiometry for R&D and Practical Applications in 2025

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Point-of-Care Diagnostics and Devices".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 844

Special Issue Editor


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Guest Editor
1. School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
2. Biological Systems Unit, Okinawa Institute Science and Technology, Okinawa 904-0495, Japan
Interests: systems biology and medicine; microwave radiometry; metagenomics
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Special Issue Information

Dear Colleagues,

This Special Issue of Diagnostics is dedicated to medical microwave radiometry for R&D and its practical application. Unlike infrared thermography, which visualizes the temperature of the skin, microwave radiometry (MWR) is based on the measurement of the tissue's own electromagnetic radiation in the microwave range. It allows for non-invasive detection of thermal anomalies in internal tissues at a depth of several centimeters. MWR can be used for non-invasive monitoring of the temperature of internal tissues during hypo- and hyperthermia. It is known that the temperature of a malignant tumor depends on its growth rate; therefore, the temperature of the tumor is a natural indicator of the aggressiveness of the tumor. Diseases associated with inflammatory processes in internal tissues are subjects of research in combination with MWR. In addition, the technology can be used not only for diagnostics but also to monitor the course of the treatment of diseases that are accompanied by changes in the temperature of internal tissues. Almost all human organs could be examined by MWR. The availability of inexpensive, big MWR data has attracted the interest of machine learning specialists in order to improve the sensitivity and specificity of the method.

Prof. Dr. Igor Goryanin
Guest Editor

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Keywords

  • microwave radiometry
  • core body temperature
  • breast cancer detection
  • artificial intelligence
  • COVID-19 pneumonia
  • vulnerable plaque
  • carotid
  • hypothermia
  • hyperthermia
  • ischemic stroke
  • traumatic brain injury
  • rheumatoid arthritis
  • diabetic foot
  • brown adipose tissue activity
  • vesicoureteral reflux

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Published Papers (1 paper)

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Research

18 pages, 3245 KiB  
Article
Breast Cancer Detection via Multi-Tiered Self-Contrastive Learning in Microwave Radiometric Imaging
by Christoforos Galazis, Huiyi Wu and Igor Goryanin
Diagnostics 2025, 15(5), 549; https://doi.org/10.3390/diagnostics15050549 - 25 Feb 2025
Viewed by 623
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 [...] Read more.
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. Full article
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