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Mammography with deep learning for breast cancer detection
(2024) Wang, Lulu; Department of Engineering
X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.
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Integrating Spatial Omics and Deep Learning : Toward Predictive Models of Cardiomyocyte Differentiation Efficiency
(2025-10) Kgabeng, Tumo; Wang, Lulu; Ngwangwa, Harry M.; Pandelani, Thanyani; Department of Engineering
Advances in cardiac regenerative medicine increasingly rely on integrating artificial intelligence with spatial multi-omics technologies to decipher intricate cellular dynamics in cardiomyocyte differentiation. This systematic review, synthetising insights from 88 PRISMA selected studies spanning 2015–2025, explores how deep learning architectures, specifically Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs), synergise with multi-modal single-cell datasets, spatially resolved transcriptomics, and epigenomics to advance cardiac biology. Innovations in spatial omics technologies have revolutionised our understanding of the organisation of cardiac tissue, revealing novel cellular communities and metabolic landscapes that underlie cardiovascular health and disease. By synthesising cutting-edge methodologies and technical innovations across these 88 studies, this review establishes the foundation for AI-enabled cardiac regeneration, potentially accelerating the clinical adoption of regenerative treatments through improved therapeutic prediction models and mechanistic understanding. We examine deep learning implementations in spatiotemporal genomics, spatial multi-omics applications in cardiac tissues, cardiomyocyte differentiation challenges, and predictive modelling innovations that collectively advance precision cardiology and next-generation regenerative strategies.
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Aharonov-Bohm and Altshuler-Aronov-Spivak oscillations in the quasiballistic regime in phase-pure GaAs/InAs core/shell nanowires
(2025-08) Basaric, Farah; Brajovic, Vladan; Behner, Gerrit; Moors, Kristof; Schaarman, William; Manolescu, Andrei; Juluri, Raghavendra; Sanchez, Ana M.; Bae, Jin Hee; Lüth, Hans; Grützmacher, Detlev; Pawlis, Alexander; Schäpers, Thomas; Department of Engineering
The realization of various qubit systems based on high-quality hybrid superconducting quantum devices is often achieved using semiconductor nanowires. For such hybrid devices, a good coupling between the superconductor and the conducting states in the semiconductor wire is crucial. GaAs/InAs core/shell nanowires with an insulating core and a conductive InAs shell fulfill this requirement, since the electronic states are strongly confined near the surface. However, maintaining a good crystal quality in the conducting shell is a challenge for this type of nanowire. In this work, we present phase-pure zinc-blende GaAs/InAs core/shell nanowires and analyze their low-temperature magnetotransport properties. We observe pronounced magnetic flux quantum periodic oscillations, which can be attributed to a combination of Aharonov-Bohm and Altshuler-Aronov-Spivak oscillations. From the gate and temperature dependence of the conductance oscillations, as well as from supporting theoretical transport calculations, we conclude that the conducting states in the shell are in the quasiballistic transport regime, with few scattering centers, but nevertheless leading to an Altshuler-Aronov-Spivak correction that dominates at small magnetic field strengths. Our results demonstrate that phase-pure zinc-blende GaAs/InAs core/shell nanowires represent a very promising alternative semiconductor-nanowire-based platform for hybrid quantum devices.
Verk
Exploring the Potential of Sensing for Breast Cancer Detection
(2023-09) Chowdhury, Nure Alam; Wang, Lulu; Gu, Linxia; Kaya, Mehmet; Department of Engineering
Breast cancer is a generalized global problem. Biomarkers are the active substances that have been considered as the signature of the existence and evolution of cancer. Early screening of different biomarkers associated with breast cancer can help doctors to design a treatment plan. However, each screening technique for breast cancer has some limitations. In most cases, a single technique can detect a single biomarker at a specific time. In this study, we address different types of biomarkers associated with breast cancer. This review article presents a detailed picture of different techniques and each technique’s associated mechanism, sensitivity, limit of detection, and linear range for breast cancer detection at early stages. The limitations of existing approaches require researchers to modify and develop new methods to identify cancer biomarkers at early stages.
Verk
Deep Learning Techniques to Diagnose Lung Cancer
(2022-11) Wang, Lulu; Department of Engineering
Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.