Breast microwave sensing (BMS) is an emerging modality that has the potential to be used as a breast cancer screening technique but challenges remain before the modality is suitable for clinical use. Improvements to image-based and machine learning tumor-detection methods are required. This thesis presents novel improvements in image reconstruction and machine learning methods. This work presents the development of the largest open-access experimental dataset in the published BMS literature to-date, the University of Manitoba Breast Microwave Imaging Dataset (UM-BMID). The impact of the inverse chirp z-transform (ICZT) on radar-based image reconstruction was compared to that of the standard inverse discrete Fourier transform using a subset of this dataset. The ICZT was found to reduce image artifacts, improve image contrast, and increase tumor-detection in reconstructions. A novel reconstruction method, the iterative delay-and-sum (itDAS) beamformer, was compared to two literature standard approaches. The novel method improved image contrast by as much as 249% on average and allows for the implementation of correction factors to improve the radar signal model used in the literature standard algorithms. Three correction factors were examined and modeling the output pulse of the BMS system significantly increased the contrast of itDAS reconstructions. The diagnostic capability of machine learning methods in BMS was investigated using UM-BMID. The area under the curve of the receiver operating characteristic curve of a convolutional neural network was estimated to be between (76 ± 3)% and (91 ± 3)%, where the upper estimate is obtained when the testing set is constrained to consist of phantoms with breast volumes that are within the breast volume bounds of the training set and when the tumor is located at the same vertical position as the antennas. This thesis has set the stage for future large-scale analyses in BMS through the development of the first and largest open-access dataset in the published literature and through the promising results obtained with the application of machine learning methods and the novel itDAS beamformer.

This reference text explores cutting edge research into the detection of breast cancer using Microwave Imaging. Early breast cancer detection is vital for reducing mortality rates. Within this book Microwave scattering and microwave imaging based cancer detection are analysed as well as breast anatomy and breast cancer types. The book discusses 3-D level set based optimization as well as the Finite difference time domain (FDTD) technique. Advanced methods in image reconstruction techniques and Group Theory are explained with application to computation reduction. Machine learning-based advanced methods are also described for breast cancer detection. This book is highly useful for the academic community working in biomedical imaging, electromagnetic and microwave imaging, breast cancer imaging, inverse scattering and optimization. Key Features: Breast cancer screening techniques are described and with advantages and disadvantages Multiple frequency inverse scattering is discussed Microwave imaging basics with detection analysis are explained in detail Includes 3-D level set based optimization Presents advanced methods on image-based reconstruction techniques

The development of an accurate, efficient, portable, and affordable method for identifying breast cancer is critical for both early detection and improved prognosis. Medical imaging modalities play a critical role in cancer screening and treatment monitoring. Diffuse optical tomography (DOT) is a non-invasive imaging modality that can be used in a low-complexity probe design, resulting in an inexpensive portable imaging diagnostic device with low power consumption. In recent years, machine learning techniques have created transformative opportunities for medical image reconstruction and analysis, helping move toward data-driven algorithm designs wherein computational power is augmented with physics priors to push the accuracy and fairness of image driven diagnosis to new limits. In this thesis, we present multiple deep learning-based medical image reconstruction and analysis approaches for screening breast cancer lesions acquired by DOT. First, an end-to-end image reconstruction model from sensor-domain data is proposed, where physics-based simulation is leveraged to address the lack of available real-world data required for training. Next, we adopt a transfer learning strategy to align and translate the sensor domain distribution between in silico and real-world data and propose a novel loss to promote appearance similarity and penalize artifacts. Following up on this we propose a joint reconstruction and localization solution that simultaneously attends to the most important features while ensuring better lesion localization. Finally, we propose an orthogonal multi-frequency fusion solution for direct prediction of the end task from sensor signal data, increasing diagnosis accuracy at a reduced computational cost. Extending a portable device with such diagnosis ability promises to improve first-line treatment throughput. These contributions demonstrate the promising role of deep learning in DOT image reconstruction and diagnosis. Combined, our contributions open the path towards personalized medicine for non-invasive portable diagnosis and treatment monitoring of breast cancer in the very near future.

This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images comprehensively examines the wide range of AI-based mammogram analysis methods for medical applications. Beginning with an introductory overview of mammogram data analysis, the book covers the current technologies such as ultrasound, molecular breast imaging (MBI), magnetic resonance (MR), and Positron Emission mammography (PEM), as well as the recent advancements in 3D breast tomosynthesis and 4D mammogram. Deep learning models are presented in each chapter to show how they can assist in the efficient processing of breast images. The book also discusses hybrid intelligence approaches for early-stage detection and the use of machine learning classifiers for cancer detection, staging and density assessment in order to develop a proper treatment plan. This book will not only aid computer scientists and medical practitioners in developing a real-time AI based mammogram analysis system, but also addresses the issues and challenges with the current processing methods which are not conducive for real-time applications. Presents novel ideas for AI based mammogram data analysis Discusses the roles deep learning and machine learning techniques play in efficient processing of mammogram images and in the accurate defining of different types of breast cancer Features dozens of real-world case studies from contributors across the globe

This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography, and deep learning for general image reconstruction.

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction.