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Communication Dans Un Congrès Année : 2023

Electromagnetic Breast Imaging and Uncertainty Quantification with Bayesian Neural Networks

Résumé

Different scenarios of breast imaging segmentation and estimation of its electromagnetic (EM) or/and ultrasonic (US) parameters are presented, part of a two-pronged dynamic using Bayesian Neural Networks, the first being to continue obtaining more and more qualitative results, while the second is to propose a precise and detailed framework for the choices made on the basis of the underlying physics and/or empirical behavioral observations of the a priori distributions of Bayesian neural network parameters. We propose a methodological participation applicable to Bayesian convolutional neural networks, showing the efficiency of such an approach with simulated and real EM or/and US breast imaging datasets. A Bayesian data fusion framework (e.g.multi-frequency data and multi-physics data) is hence introduced since EM low-resolution and US high-resolution imaging complementarity has been shown to improve the quality of the reconstructed image, providing well-contrasted zones.
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Dates et versions

hal-04108645 , version 1 (20-11-2023)

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  • HAL Id : hal-04108645 , version 1

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Valentin Noël, Thomas Rodet, Dominique Lesselier. Electromagnetic Breast Imaging and Uncertainty Quantification with Bayesian Neural Networks. Progress In Electromagnetics Research Symposium, PIERS, Jul 2023, Prague (Czech Republic), Czech Republic. ⟨hal-04108645⟩
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