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| http://dx.doi.org/10.1002/mdc3.13428 | DOI Listing |
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Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.
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Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia. Electronic address:
- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications.
View Article and Find Full Text PDFDeep learning classification of MGMT status of glioblastomas using multiparametric MRI with a novel domain knowledge augmented mask fusion approach.
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Department of Electrical Electronical Engineering, Yaşar University, Bornova, İzmir, Turkey.
We aimed to build a robust classifier for the MGMT methylation status of glioblastoma in multiparametric MRI. We focused on multi-habitat deep image descriptors as our basic focus. A subset of the BRATS 2021 MGMT methylation dataset containing both MGMT class labels and segmentation masks was used.
View Article and Find Full Text PDFPhysical unclonable in-memory computing for simultaneous protecting private data and deep learning models.
Nat Commun
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Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China.
Compute-in-memory based on resistive random-access memory has emerged as a promising technology for accelerating neural networks on edge devices. It can reduce frequent data transfers and improve energy efficiency. However, the nonvolatile nature of resistive memory raises concerns that stored weights can be easily extracted during computation.
View Article and Find Full Text PDFA Joint three-plane Physics-constrained Deep learning based Polynomial Fitting Approach for MR Electrical Properties Tomography.
Neuroimage
January 2025
Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea. Electronic address:
Magnetic resonance electrical properties tomography can extract the electrical properties of in-vivo tissue. To estimate tissue electrical properties, various reconstruction algorithms have been proposed. However, physics-based reconstructions are prone to various artifacts such as noise amplification and boundary artifact.
View Article and Find Full Text PDFDynamic cycles between brain states during creative storytelling.
Neuroimage
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State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China. Electronic address:
Many theories suggest that creative thinking involves a dynamic transition between different mental states, yet empirical evidence supporting this notion remains scarce. The dual process model proposes that spontaneous thinking and deliberate thinking drive the dwell in and the transitions between different mental states during creative thinking, but there is a debate over whether the two types of thinking operate in parallel or in sequence. To address these gaps, we conducted a functional magnetic resonance imaging (fMRI) study in 41 college students during a creative storytelling task.
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