ArticleBioengineering (Basel, Switzerland)2022
An End-to-End Deep Learning Approach for Quantitative Microwave Breast Imaging in Real-Time Applications.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Tumor Detection and Characterization Using Microwave Imaging Technique-An Experimental Calibration Approach.Sensors (Basel, Switzerland) · 2026Article
- Conformal On-Body Antenna System Integrated with Deep Learning for Non-Invasive Breast Cancer Detection.Sensors (Basel, Switzerland) · 2025Article
- Application of Spectral Approach Combined with U-NETs for Quantitative Microwave Breast Imaging.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning in Microwave Medical Imaging and Lesion Detection.Diagnostics (Basel, Switzerland) · 2025Review
- Magnetoencephalographic source localization and reconstruction via deep learning.Frontiers in neuroscience · 2025Article
- Training Universal Deep-Learning Networks for Electromagnetic Medical Imaging Using a Large Database of Randomized Objects.Sensors (Basel, Switzerland) · 2023Article
- Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions.Bioengineering (Basel, Switzerland) · 2023Article
- A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography.Diagnostics (Basel, Switzerland) · 2023Article
- Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network.Bioengineering (Basel, Switzerland) · 2023Article
- Radar-Based Microwave Breast Imaging Using Neurocomputational Models.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: In this paper, an artificial neural network approach for effective and real-time quantitative microwave breast imaging is proposed. It proposes some numerical analyses for the optimization of the network architecture and the improvement of recovery performance and processing time in the microwave breast imaging framework, which represents a fundamental preliminary step for future diagnostic applications. (2) Methods: The methodological analysis of the proposed approach is based on two main aspects: firstly, the definition and generation of a proper database adopted for the training of the neural networks and, secondly, the design and analysis of different neural network architectures. (3) Results: The methodology was tested in noisy numerical scenarios with different values of SNR showing good robustness against noise. The results seem very promising in comparison with conventional nonlinear inverse scattering approaches from a qualitative as well as a quantitative point of view. (4) Conclusion: The use of quantitative microwave imaging and neural networks can represent a valid alternative to (or completion of) modern conventional medical imaging techniques since it is cheaper, safer, fast, and quantitative, thus suitable to assist medical decisions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.